Academic literature on the topic 'Generative sequence models'
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Journal articles on the topic "Generative sequence models"
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Wang, Yongkang, Xuan Liu, Feng Huang, Zhankun Xiong, and Wen Zhang. "A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 3–11. http://dx.doi.org/10.1609/aaai.v38i1.27749.
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Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with inter-contrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-the-art deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.
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Wu, Zachary, Kadina E. Johnston, Frances H. Arnold, and Kevin K. Yang. "Protein sequence design with deep generative models." Current Opinion in Chemical Biology 65 (December 2021): 18–27. http://dx.doi.org/10.1016/j.cbpa.2021.04.004.
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Akl, Hoda, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez, and Purushottam D. Dixit. "GENERALIST: A latent space based generative model for protein sequence families." PLOS Computational Biology 19, no. 11 (November 27, 2023): e1011655. http://dx.doi.org/10.1371/journal.pcbi.1011655.
Full textAbstract:
Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability.
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Feinauer, Christoph, Barthelemy Meynard-Piganeau, and Carlo Lucibello. "Interpretable pairwise distillations for generative protein sequence models." PLOS Computational Biology 18, no. 6 (June 23, 2022): e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.
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Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise models from more complex ones using an energy-based modeling framework. We show that for the tested models the extracted pairwise models can replicate the energies of the original models and are also close in performance in tasks like mutational effect prediction. In addition, we show that even simpler, factorized models often come close in performance to the original models.
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Won, K. J., C. Saunders, and A. Prügel-Bennett. "Evolving Fisher Kernels for Biological Sequence Classification." Evolutionary Computation 21, no. 1 (March 2013): 83–105. http://dx.doi.org/10.1162/evco_a_00065.
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Fisher kernels have been successfully applied to many problems in bioinformatics. However, their success depends on the quality of the generative model upon which they are built. For Fisher kernel techniques to be used on novel problems, a mechanism for creating accurate generative models is required. A novel framework is presented for automatically creating domain-specific generative models that can be used to produce Fisher kernels for support vector machines (SVMs) and other kernel methods. The framework enables the capture of prior knowledge and addresses the issue of domain-specific kernels, both of which are current areas that are lacking in many kernel-based methods. To obtain the generative model, genetic algorithms are used to evolve the structure of hidden Markov models (HMMs). A Fisher kernel is subsequently created from the HMM, and used in conjunction with an SVM, to improve the discriminative power. This paper investigates the effectiveness of the proposed method, named GA-SVM. We show that its performance is comparable if not better than other state of the art methods in classifying secretory protein sequences of malaria. More interestingly, it showed better results than the sequence-similarity-based approach, without the need for additional homologous sequence information in protein enzyme family classification. The experiments clearly demonstrate that the GA-SVM is a novel way to find features with good performance from biological sequences, that does not require extensive tuning of a complex model.
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Liu, Yitian, and Zhouhui Lian. "DeepCalliFont: Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 3774–82. http://dx.doi.org/10.1609/aaai.v38i4.28168.
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Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed methods can synthesize high-quality Chinese glyph images. However, glyphs in calligraphy font styles often do not meet these assumptions. To address this problem, we propose a novel model, DeepCalliFont, for few-shot Chinese calligraphy font synthesis by integrating dual-modality generative models. Specifically, the proposed model consists of image synthesis and sequence generation branches, generating consistent results via a dual-modality representation learning strategy. The two modalities (i.e., glyph images and writing sequences) are properly integrated using a feature recombination module and a rasterization loss function. Furthermore, a new pre-training strategy is adopted to improve the performance by exploiting large amounts of uni-modality data. Both qualitative and quantitative experiments have been conducted to demonstrate the superiority of our method to other state-of-the-art approaches in the task of few-shot Chinese calligraphy font synthesis. The source code can be found at https://github.com/lsflyt-pku/DeepCalliFont.
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Safranchik, Esteban, Shiying Luo, and Stephen Bach. "Weakly Supervised Sequence Tagging from Noisy Rules." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5570–78. http://dx.doi.org/10.1609/aaai.v34i04.6009.
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We propose a framework for training sequence tagging models with weak supervision consisting of multiple heuristic rules of unknown accuracy. In addition to supporting rules that vote on tags in the output sequence, we introduce a new type of weak supervision, called linking rules, that vote on how sequence elements should be grouped into spans with the same tag. These rules are an alternative to candidate span generators that require significantly more human effort. To estimate the accuracies of the rules and combine their conflicting outputs into training data, we introduce a new type of generative model, linked hidden Markov models (linked HMMs), and prove they are generically identifiable (up to a tag permutation) without any observed training labels. We find that linked HMMs provide an average 7 F1 point boost on benchmark named entity recognition tasks versus generative models that assume the tags are i.i.d. Further, neural sequence taggers trained with these structure-aware generative models outperform comparable state-of-the-art approaches to weak supervision by an average of 2.6 F1 points.
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Polceanu, Mihai, Julie Porteous, Alan Lindsay, and Marc Cavazza. "Narrative Plan Generation with Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.
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Narrative Generation has attracted significant interest as a novel application of Automated Planning techniques. However, the vast amount of narrative material available opens the way to the use of Deep Learning techniques. In this paper, we explore the feasibility of narrative generation through self-supervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. We use datasets of well-formed plots generated by a narrative planning approach, using pre-existing, published, narrative planning domains, to train generative models. Our experiments demonstrate the ability of generative sequence models to produce narrative plots with similar structure to those obtained with planning techniques, but with significant plot novelty in comparison with the training set. Most importantly, generated plots share structural properties associated with narrative quality measures used in Planning-based methods. As plan-based structures account for a higher level of causality and narrative consistency, this suggests that our approach is able to extend a set of narratives with novel sequences that display the same high-level narrative properties. Unlike methods developed to extend sets of textual narratives, ours operates at the level of plot structure. Thus, it has the potential to be used across various media for plots of significant complexity, being initially limited to training and generation operating in the same narrative genre.
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Zhang, Zhiyuan, and Zhanshan Wang. "Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network." Mathematics 11, no. 20 (October 19, 2023): 4350. http://dx.doi.org/10.3390/math11204350.
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Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple target load sequences. Existing data-driven methods for load forecasting in integrated energy systems use multi-task learning to address these challenges. When determining the input data for multi-task learning, existing research primarily relies on data correlation analysis and considers the influence of external environmental factors in terms of feature engineering. However, such feature engineering methods lack the utilization of the characteristics of multi-target sequences. In leveraging the characteristics of multi-target sequences, language generation models trained on textual logic structures and other sequence features can generate synthetic data that can even be applied to self-training to improve model performance. This provides an idea for feature engineering in data-driven time-series forecasting models. However, because time-series data are different from textual data, existing transformer-based language generation models cannot be directly applied to generating time-series data. In order to consider the characteristics of multi-target load sequences in integrated energy system load forecasting, this paper proposed a generative tractive network (GTN) model. By selectively utilizing appropriate autoregressive feature data for temporal data, this model facilitates feature mining from time-series data. This model is capable of analyzing temporal data variations, generating novel synthetic time-series data that align with the intrinsic temporal patterns of the original sequences. Moreover, the model can generate synthetic samples that closely mimic the variations in the original time series. Subsequently, through the integration of the GTN and autoregressive feature data, various prediction models are employed in case studies to affirm the effectiveness of the proposed methodology.
10
Hawkins-Hooker, Alex, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, and David Bikard. "Generating functional protein variants with variational autoencoders." PLOS Computational Biology 17, no. 2 (February 26, 2021): e1008736. http://dx.doi.org/10.1371/journal.pcbi.1008736.
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The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequence data have been shown to learn biologically meaningful representations helpful for a variety of downstream tasks, but their potential for direct use in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the luxA bacterial luciferase. We propose separate VAE models to work with aligned sequence input (MSA VAE) and raw sequence input (AR-VAE), and offer evidence that while both are able to reproduce patterns of amino acid usage characteristic of the family, the MSA VAE is better able to capture long-distance dependencies reflecting the influence of 3D structure. To confirm the practical utility of the models, we used them to generate variants of luxA whose luminescence activity was validated experimentally. We further showed that conditional variants of both models could be used to increase the solubility of luxA without disrupting function. Altogether 6/12 of the variants generated using the unconditional AR-VAE and 9/11 generated using the unconditional MSA VAE retained measurable luminescence, together with all 23 of the less distant variants generated by conditional versions of the models; the most distant functional variant contained 35 differences relative to the nearest training set sequence. These results demonstrate the feasibility of using deep generative models to explore the space of possible protein sequences and generate useful variants, providing a method complementary to rational design and directed evolution approaches.
Dissertations / Theses on the topic "Generative sequence models"
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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.
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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and one in South America. In northern europe, an error of one taxi or less per hour per zone was achieved in 64% of the cases, in South America the number was 40%. In both cities, it beat the SARIMA and stacked ensemble benchmarks. This performance has been achieved by tuning the hyperparameters with a Bayesian optimization algorithm. Additionally, weather and holiday features were added as input features in the northern European city and they did not improve the accuracy of TDNet.
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Goodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.
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The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a method of floorplan generation using two machine learning models: a sequential model that generates rooms within the floorplan, and a graph-based model that finds adjacencies between generated rooms. Each of these models can be altered such that they are each capable of producing a floorplan independently; however, we find that the combination of these models outperforms each of its pieces, as well as a statistic-based approach.
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Tubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
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Les Machines de Boltzmann restreintes (RBM) sont des modèles graphiques capables d’apprendre simultanément une distribution de probabilité et une représentation des données. Malgré leur architecture relativement simple, les RBM peuvent reproduire très fidèlement des données complexes telles que la base de données de chiffres écrits à la main MNIST. Il a par ailleurs été montré empiriquement qu’elles peuvent produire des représentations compositionnelles des données, i.e. qui décomposent les configurations en leurs différentes parties constitutives. Cependant, toutes les variantes de ce modèle ne sont pas aussi performantes les unes que les autres, et il n’y a pas d’explication théorique justifiant ces observations empiriques. Dans la première partie de ma thèse, nous avons cherché à comprendre comment un modèle si simple peut produire des distributions de probabilité si complexes. Pour cela, nous avons analysé un modèle simplifié de RBM à poids aléatoires à l’aide de la méthode des répliques. Nous avons pu caractériser théoriquement un régime compositionnel pour les RBM, et montré sous quelles conditions (statistique des poids, choix de la fonction de transfert) ce régime peut ou ne peut pas émerger. Les prédictions qualitatives et quantitatives de cette analyse théorique sont en accord avec les observations réalisées sur des RBM entraînées sur des données réelles. Nous avons ensuite appliqué les RBM à l’analyse et à la conception de séquences de protéines. De part leur grande taille, il est en effet très difficile de simuler physiquement les protéines, et donc de prédire leur structure et leur fonction. Il est cependant possible d’obtenir des informations sur la structure d’une protéine en étudiant la façon dont sa séquence varie selon les organismes. Par exemple, deux sites présentant des corrélations de mutations importantes sont souvent physiquement proches sur la structure. A l’aide de modèles graphiques tels que les Machine de Boltzmann, on peut exploiter ces signaux pour prédire la proximité spatiale des acides-aminés d’une séquence. Dans le même esprit, nous avons montré sur plusieurs familles de protéines que les RBM peuvent aller au-delà de la structure, et extraire des motifs étendus d’acides aminés en coévolution qui reflètent les contraintes phylogénétiques, structurelles et fonctionnelles des protéines. De plus, on peut utiliser les RBM pour concevoir de nouvelles séquences avec des propriétés fonctionnelles putatives par recombinaison de ces motifs. Enfin, nous avons développé de nouveaux algorithmes d’entraînement et des nouvelles formes paramétriques qui améliorent significativement la performance générative des RBM. Ces améliorations les rendent compétitives avec l’état de l’art des modèles génératifs tels que les réseaux génératifs adversariaux ou les auto-encodeurs variationnels pour des données de taille intermédiairesRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
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Rehn, Martin. "Aspects of memory and representation in cortical computation." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4161.
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Denna avhandling i datalogi föreslår modeller för hur vissa beräkningsmässiga uppgifter kan utföras av hjärnbarken. Utgångspunkten är dels kända fakta om hur en area i hjärnbarken är uppbyggd och fungerar, dels etablerade modellklasser inom beräkningsneurobiologi, såsom attraktorminnen och system för gles kodning. Ett neuralt nätverk som producerar en effektiv gles kod i binär mening för sensoriska, särskilt visuella, intryck presenteras. Jag visar att detta nätverk, när det har tränats med naturliga bilder, reproducerar vissa egenskaper (receptiva fält) hos nervceller i lager IV i den primära synbarken och att de koder som det producerar är lämpliga för lagring i associativa minnesmodeller. Vidare visar jag hur ett enkelt autoassociativt minne kan modifieras till att fungera som ett generellt sekvenslärande system genom att utrustas med synapsdynamik. Jag undersöker hur ett abstrakt attraktorminnessystem kan implementeras i en detaljerad modell baserad på data om hjärnbarken. Denna modell kan sedan analyseras med verktyg som simulerar experiment som kan utföras på en riktig hjärnbark. Hypotesen att hjärnbarken till avsevärd del fungerar som ett attraktorminne undersöks och visar sig leda till prediktioner för dess kopplingsstruktur. Jag diskuterar också metodologiska aspekter på beräkningsneurobiologin idag.In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.
QC 20100916
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Shimagaki, Kai. "Advanced statistical modeling and variable selection for protein sequences." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS548.
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Au cours des dernières décennies, des techniques de séquençage de protéines ont été développées et des expériences continues ont été menées. Grâce à tous ces efforts, de nos jours, nous avons obtenu plus de deux-cents millions données relative à des séquences de protéines. Afin de traiter une telle quantité de données biologiques, nous avons maintenant besoin de théories et de technologies pour extraire des informations de ces données que nous pouvons comprendre et pour apporter des idées. L'idée clé pour résoudre ce problème est la physique statistique et l'état de l'art de le Machine Learning (ML). La physique statistique est un domaine de la physique qui peut décrire avec succès de nombreux systèmes complexes en extrayant ou en réduisant les variables pour en faire des variables interprétables basées sur des principes simples.ML, d'autre part, peut représenter des données (par exemple en les reconstruisant ou en les classifiant) sans comprendre comment les données ont été générées, c'est-à-dire le phénomène physique à l'origine de la création de ces données. Dans cette thèse, nous rapportons des études de modélisation générative de séquences protéiques et de prédictions de contacts protéines-résidus à l'aide de la modélisation statistique inspirée de la physique et de méthodes orientées ML. Dans la première partie, nous passons en revue le contexte général de la biologie et de la génomique. Ensuite, nous discutons des modélisations statistiques pour la séquence des protéines. En particulier, nous passons en revue l'analyse de couplage direct (DCA), qui est la technologie de base de notre rechercheOver the last few decades, protein sequencing techniques have been developed and continuous experiments have been done. Thanks to all of these efforts, nowadays, we have obtained more than two hundred million protein sequence data. In order to deal with such a huge amount of biological data, now, we need theories and technologies to extract information that we can understand and interpret.The key idea to resolve this problem is statistical physics and the state of the art of machine learning (ML). Statistical physics is a field of physics that can successfully describe many complex systems by extracting or reducing variables to be interpretable variables based on simple principles. ML, on the other hand, can represent data (such as reconstruction and classification) without assuming how the data was generated, i.e. physical phenomenon behind of data. In this dissertation, we report studies of protein sequence generative modeling and protein-residue contact predictions using statistical physics-inspired modeling and ML-oriented methods. In the first part, we review the general background of biology and genomics. Then we discuss statistical modelings for protein sequence. In particular, we review Direct Coupling Analysis (DCA), which is the core technology of our research. We also discuss the effects of higher-order statistics contained in protein sequences and introduces deep learning-based generative models as a model that can go beyond pairwise interaction
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Adak, Bulent Mehmet. "Model-based Code Generation For The High Level Architecture Federates." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12609032/index.pdf.
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We tackle the problem of automated code generation for a High Level Architecture (HLA)-
compliant federate application, given a model of the federation architecture including the
federate&rsquos behavior model. The behavior model is based on Live Sequence Charts (LSCs), adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). The FAMM is constructed conforming to metaGME, the meta-metamodel offered by Generic Modeling Environment (GME). FAMM serves as a formal language for describing federation architectures. We present a code generator that generates Java/AspectJ code directly from a federation architecture model. An objective is to help verify a federation architecture by testing it early in the development lifecycle. Another objective is to help developers construct complete federate applications. Our approach to achieve these objectives is aspect-oriented in that the code generated from the LSC in conjunction with the Federation Object Model (FOM) serves as the base code on which the computation logic is weaved as an aspect.
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Kunst, Rafael. "Um injetor de erros aplicado à avaliação de desempenho do codificador de canal em redes IEEE 802.16." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17800.
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A necessidade de suportar serviços multimídia impulsiona o desenvolvimento das redes sem fio. Com isso, torna-se importante fornecer confiabilidade na transmissão de dados em um ambiente sujeito a variações espaciais, temporais e de freqüência, causadas por fenômenos físicos que, geralmente, causam erros nos dados transmitidos. Esses erros são basicamente de dois tipos: erros em rajada e erros aleatórios (Additive White Gaussian Noise - AWGN). Simular o comportamento dos canais sem fio afetados por erros é objeto de pesquisa há diversos anos. Entretanto, grande parte das pesquisas não considera a aplicação dos dois tipos de erros simultaneamente, o que pode gerar imprecisões nos resultados das simulações. Sendo assim, este trabalho propõe um injetor capaz de gerar tanto seqüências de erros em rajada quanto AWGN, além de propor um modelo de erros híbrido que considera a injeção de ambos os tipos de erros para simular o comportamento de um canal sem fio. O injetor de erros proposto é empregado em um estudo de caso referente à análise de desempenho do mecanismo de codificação de canal em redes que seguem o padrão IEEE 802.16, tanto nomádicas (fixas) quanto móveis. É avaliada a capacidade de correção dos codificadores Forward Error Correction (FEC), de emprego obrigatório de acordo com o referido padrão. Além disso, estuda-se o impacto causado pela aplicação de técnicas que consistem na adição de diversidade temporal à transmissão, em cenários cuja ocorrência dos erros é em rajada, e em cenários cujos erros são modelados de acordo com seqüências AWGN. Finalmente, realiza-se um estudo teórico sobre a vazão que pode ser atingida nos cenários nomádicos e móveis, além de uma discussão sobre os avanços tecnológicos trazidos pela multiplexação de canal baseada em Orthogonal Frequency Division Multiple Access (OFDMA), empregado em redes IEEE 802.16 móveis.The demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.
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Künstner, Axel. "Birds as a Model for Comparative Genomic Studies." Doctoral thesis, Uppsala universitet, Evolutionsbiologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-159766.
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Comparative genomics provides a tool to investigate large biological datasets, i.e. genomic datasets. In my thesis I focused on inferring patterns of selection in coding and non-coding regions of avian genomes. Until recently, large comparative studies on selection were mainly restricted to model species with sequenced genomes. This limitation has been overcome with advances in sequencing technologies and it is now possible to gather large genomic data sets for non-model species. Next-generation sequencing data was used to study patterns of nucleotide substitutions and from this we inferred how selection has acted in the genomes of 10 non-model bird species. In general, we found evidence for a negative correlation between neutral substitution rate and chromosome size in birds. In a follow up study, we investigated two closely related bird species, to study expression levels in different tissues and pattern of selection. We found that between 2% and 18% of all genes were differentially expressed between the two species. We showed that non-coding regions adjacent to genes are under evolutionary constraint in birds, which suggests that noncoding DNA plays an important functional role in the genome. Regions downstream to genes (3’) showed particularly high level of constraint. The level of constraint in these regions was not correlated to the length of untranslated regions, which suggests that other causes play also a role in sequence conservation. We compared the rate of nonsynonymous substitutions to the rate of synonymous substitutions in order to infer levels of selection in protein-coding sequences. Synonymous substitutions are often assumed to evolve neutrally. We studied synonymous substitutions by estimating constraint on 4-fold degenerate sites of avian genes and found significant evolutionary constraint on this category of sites (between 24% and 43%). These results call for a reappraisal of synonymous substitution rates being used as neutral standards in molecular evolutionary analysis (e.g. the dN/dS ratio to infer positive selection). Finally, the problem of sequencing errors in next-generation sequencing data was investigated. We developed a program that removes erroneous bases from the reads. We showed that low coverage sequencing projects and large genome sequencing projects will especially gain from trimming erroneous reads.
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Alsafi, Radi Taha M. "Generation of complex recombinant fowlpox virus 9 (FP9) encoding simian immunodeficiency virus (SIVmac239) sequences as a model HIV vaccine candidate." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/generation-of-complex-recombinant-fowlpox-virus-9-fp9-encoding-simian-immunodeficiency-virus-sivmac239-sequences-as-a-model-hiv-vaccine-candidate(1a015762-8dc2-4153-a586-d7fab88b9658).html.
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The development of a safe and effective HIV vaccine remains challenging due to its high antigenic variability. Poxviruses are large, stable, and have a track record of use as human vaccine candidates. Recombinant fowlpox virus 9 (rFP9), a highly attenuated host range-restricted poxvirus strain, has been safely administered to humans with no ill effects, and is known to be immunogenic. This thesis describes the construction of complex rFP9 encoding various sequences of SIVmac239. The SIVmac239/macaque model is widely used for HIV vaccine development. The ultimate aim of this work was to combine the advantages of FP9 with those of live attenuated SIV to produce a safe yet hopefully effective model HIV vaccine candidate. Transfer plasmids for five different insertion sites within the FP9 genome were designed and constructed. Homologous recombination (HR) of adjacent FP9 sequences was employed to facilitate the integration of SIVmac239 sequences into the FP9 genome. Positive rFP9 were identified by blue colouration in presence of X-gal using a transient colour selection (TCS) technique, and the final markerless pure recombinants were confirmed by PCR. Expression of the target SIV proteins in the presence of T7 polymerase has been demonstrated by immunocytochemical (ICC) staining and Western blotting (WB) assays. Expression was also quantified by enzyme-linked immunosorbent assay (ELISA) in various cell lines at multiple time points. Five different unique rFP9 have been constructed through this project. All SIVmac239 open reading frames (ORFs) save nef have been integrated into the FP9 genome, and protein expression demonstrated where possible. Moreover, a single rFP9 vector expressing the defective SIVmac239 genome driven by T7 RNA polymerase has been successfully constructed and validated using a green fluorescent protein marker.rFP9 showed appropriate transgene expression in both avian and mammalian cells, although at different levels. The expression efficiency of rFP9 was finally compared to another attenuated poxvirus vector, modified vaccinia Ankara (MVA). Comparing the protein expression levels between rFP9 and rMVA was quite difficult because different poxvirus promoters (early/late in rFP9; intermediate in rMVA) were used to direct the transcription of the T7 RNA gene. Given this limitation, although generally higher levels of expression were seen with rFP9, this cannot be attributed to the FP9 with any certainty.
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Blazejewski, Tomasz. "Generative Models for Synthetic Biology." Thesis, 2020. https://doi.org/10.7916/d8-0xvy-cw79.
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Over the past several years, the fields of synthetic biology and machine learning have demonstrated marked advances in the scale of their capabilities and the success of their applications. The work presented in this thesis focuses on the translation of recent advances in machine learning toward new applications in synthetic biology. In particular it is argued that the needs of synthetic biology researchers and practitioners are well met by a class of generative machine learning models, and that the scale of synthetic biology capabilities allows for their successful application across multiple domains of interest.
In Chapter 1, a novel algorithm utilizing Markov Random Fields is used to, for the first time, design functional synthetic overlapping pairs of genes with potential applications for improved biological robustness and biosafety. In Chapter 2, motivated by a desire to extend the scope of protein sequence modeling to a greater range and diversity of protein sequences, a variant of a variational autoencoder model is used to project hundreds of millions of protein sequences into a continuous latent space with potentially useful representation features. Finally, in Chapter 3, we move beyond the realm of protein sequences to define a probabilistic species-specific model of regulatory sequences and explore this model’s utility for the challenging task of gene expression prediction for non-model bacterial organisms.
Machine learning models presented in this thesis represent novel applications of models traditionally applied to data in the domains of images, text or sound toward addressing challenging problems in biology. Particular attention is devoted to the challenging task of utilizing large amounts of unlabeled data present in metagenomic sequences and the genomes of poorly characterized bacteria in the hope of improving researchers’ abilities to manipulate complex biological phenomena.
Books on the topic "Generative sequence models"
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Grigorev, Anatoliy. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/22119.
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In this manual some methods and algorithms of data processing, the sequence of the solution of problems of processing and the analysis of data for creation of behavior model of an object taking into account all a component of his mathematical model are considered. Types of technological methods of use of software and hardware for the solution of tasks in this area are described. Algorithms of distributions, regressions of temporary ranks, their transformation for the purpose of receiving mathematical models and the forecast of behavior of information and economic systems (objects) are considered.
Conforms to requirements of the Federal state educational standard of the higher education of the last generation.
For students of economic specialties, experts, graduate students.
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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects).
The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms.
Meets the requirements of Federal state educational standards of higher education of the last generation.
For students of economic specialties, specialists, and graduate students.
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Narimani, Zahra, Ali Masoudi-Nejad, and Nazanin Hosseinkhan. Next Generation Sequencing and Sequence Assembly: Methodologies and Algorithms. Springer, 2013.
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Narimani, Zahra, Ali Masoudi-Nejad, and Nazanin Hosseinkhan. Next Generation Sequencing and Sequence Assembly: Methodologies and Algorithms. Springer, 2013.
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HaCohen, Ruth. Between Generation and Suspension. Edited by Yael Kaduri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199841547.013.13.
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The chapter discusses two modes of combining music and moving images that developed in modernism. The first mode, which the author termsgeneration, relates to a type of animated narrative film in which the music precedes the visual sequence which generates the will or thought (modality) that gives rise to the narrative action. “The Sorcerer’s Apprentice,” from the Disney filmFantasia, is examined as an example. In the second mode,suspension, the picture appears as if preceding the music, even if the creative order was different, or the work does not have an actual visual manifestation. The visual sequence, which appears as if deriving from the composer’s inner world, is characterized by minute occurrences, wishing to arouse as an atmosphere or “third consciousness.” The movement “Colors” from Schoenberg’sFive Pieces for an Orchestra, opus 16, is examined as an example alongside examples from film music.
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Cruse, Holk, and Malte Schilling. Pattern generation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0024.
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The faculty to generate patterns is a basic feature of living systems. This chapter concentrates on patterns used in the context of control of behavior. Spatio-temporal patterns appear as quasi-rhythmic patterns mainly in the domain of locomotion (e.g. swimming, flying, walking). Such patterns may be rooted directly in the nervous system itself, or may emerge in interaction with the environment. The examples given show simulation of the corresponding behaviors that in most cases are applied to robots (e.g. walking in an unpredictable environment). In addition, non-rhythmic patterns will be explained which are linked to internal states and are required to select specific behaviors and control behavioral sequences. Such states may be relevant for top-down attention and may or may not be accompanied with subjective experiences, then called mind patterns. Specific cases concern the application of an internal body model, as well as states characterized as cognitive or as conscious.
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Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.
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A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.
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Banovic, Nikola, Jennifer Mankoff, and Anind K. Dey. Computational Model of Human Routine Behaviours. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0015.
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Computational Interaction enables a future in which user interfaces (UI) learn about people’s behaviours by observing them and interacting with them to help people to be productive, comfortable, healthy, and safe. However, this requires technology that can accurately model people’s behaviours. This chapter focuses on human routine behaviours enacted by people as sequences of actions performed in specific situations, i.e. behaviour instances, and presents a probabilistic, generative model of human routine behaviours that can describe, reason about, and act in response to people’s behaviours. We holistically define human routine behaviours to constrain the patterns extracted from the data, match routine behaviours, and estimate the likelihood that people will perform certain actions (in different situations) in a way that matches their demonstrated preference. The chapter illustrates how computational models of routines support stakeholders in making sense of stored logs of human behaviour, and designing UIs that respond to those behaviours.
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Dutoit, Thierry, and Yannis Stylianou. Text-to-Speech Synthesis. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0017.
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This article gives an introduction to state-of-the-art text-to-speech (TTS) synthesis systems, showing both the natural language processing and the digital signal processing problems involved. Text-to-speech (TTS) synthesis is the art of designing talking machines. The article begins with brief user-oriented description of a general TTS system and comments on its commercial applications. It then gives a functional diagram of a modern TTS system, highlighting its components. It describes its morphosyntactic module. Furthermore, it examines why sentence-level phonetization cannot be achieved by a sequence of dictionary look-ups, and describes possible implementations of the phonetizer. Finally, the article describes prosody generation, outlining how intonation and duration can approximately be computed from text. Prosody refers to certain properties of the speech signal, which are related to audible changes in pitch, loudness, and syllable length. This article also introduces the two main existing categories of techniques for waveform generation: synthesis by rule and concatenative synthesis.
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Bylander, J. Superconducting Quantum Bits of Information—Coherence and Design Improvements. Edited by A. V. Narlikar. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198738169.013.18.
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This article reviews recent progress in superconducting quantum bits, including major improvements in design and coherence times. It first provides an overview of the basics of modern superconducting qubit devices and their architectures before turning to single-qubit Hamiltonians and reference frames. It then examines how decoherence originates with noise and shows how to characterize and mitigate this noise using magnetic-resonance-type pulse sequences. It also describes the first-generation superconducting qubits and the now-dominant circuit-quantum electrodynamics architecture in which qubits are coupled to microwave resonators. Finally, it considers several improved designs of superconducting qubits in which coherence times have been significantly improved by minimizing the sensitivity to fluctuating impurities and the coupling to external modes.
Book chapters on the topic "Generative sequence models"
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Theis, Julian, Ilia Mokhtarian, and Houshang Darabi. "On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization." In Lecture Notes in Business Information Processing, 281–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_21.
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AbstractProcess mining algorithms discover a process model from an event log. The resulting process model is supposed to describe all possible event sequences of the underlying system. Generalization is a process model quality dimension of interest. A generalization metric should quantify the extent to which a process model represents the observed event sequences contained in the event log and the unobserved event sequences of the system. Most of the available metrics in the literature cannot properly quantify the generalization of a process model. A recently published method called Adversarial System Variant Approximation leverages Generative Adversarial Networks to approximate the underlying event sequence distribution of a system from an event log. While this method demonstrated performance gains over existing methods in measuring the generalization of process models, its experimental evaluations have been performed under ideal conditions. This paper experimentally investigates the performance of Adversarial System Variant Approximation under non-ideal conditions such as biased and limited event logs. Moreover, experiments are performed to investigate the originally proposed sampling parameter value of the method on its performance to measure the generalization. The results confirm the need to raise awareness about the working conditions of the Adversarial System Variant Approximation method and serve to initiate future research directions.
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Ossenberg-Engels, Julius, and Vicente Grau. "Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames." In Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 109–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39074-7_12.
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Trehan, Harshit, and Fabio Di Troia. "Fake Malware Generation Using HMM and GAN." In Silicon Valley Cybersecurity Conference, 3–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96057-5_1.
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AbstractIn the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results.
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Chen, Xuguang, Hongbin Ma, Pujun Ji, Haiting Liu, and Yan Liu. "Based on GAN Generating Chaotic Sequence." In Communications in Computer and Information Science, 37–49. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_4.
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AbstractIn this paper, an adversarial encryption algorithm based on generating chaotic sequence by GAN is proposed. Starting from the poor leakage resistance of the basic adversarial encryption communication model based on GAN, the network structure was improved. Secondly, this paper used the generated adversarial network to generate chaotic-like sequences as the key K and entered the improved adversarial encryption model. The addition of the chaotic model further improved the security of the key. In the subsequent training process, the encryption and decryption party and the attacker confront each other and optimize, and then obtain a more secure encryption model. Finally, this paper analyzes the security of the proposed encryption scheme through the key and overall model security. After subsequent experimental tests, this encryption method can eliminate the chaotic periodicity to a certain extent and the model’s anti-attack ability has also been greatly improved. After leaking part of the key to the attacker, the secure communication can still be maintained.
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Paaß, Gerhard, and Sven Giesselbach. "Foundation Models for Speech, Images, Videos, and Control." In Artificial Intelligence: Foundations, Theory, and Algorithms, 313–82. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23190-2_7.
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AbstractFoundation Models are able to model not only tokens of natural language but also token elements of arbitrary sequences. For images, square image patches can be represented as tokens; for videos, we can define tubelets that span an image patch across multiple frames. Subsequently, the proven self-attention algorithms can be applied to these tokens. Most importantly, several modalities like text and images can be processed in the same sequence allowing, for instance, the generation of images from text and text descriptions from video. In addition, the models are scalable to very large networks and huge datasets. The following multimedia types are covered in the subsequent sections. Speech recognition and text-to-speech models describe the translation of spoken language into text and vice versa. Image processing has the task to interpret images, describe them by captions, and generate new images according to textual descriptions. Video interpretation aims at recognizing action in videos and describing them through text. Furthermore, new videos can be created according to a textual description. Dynamical system trajectories characterize sequential decision problems, which can be simulated and controlled. DNA and protein sequences can be analyzed with Foundation Models to predict the structure and properties of the corresponding molecules.
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Camargo, Manuel, Marlon Dumas, and Oscar González-Rojas. "Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning." In Advanced Information Systems Engineering, 55–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07472-1_4.
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AbstractBusiness process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.
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Veitaite, Ilona, and Audrius Lopata. "Knowledge-Based UML Dynamic Models Generation from Enterprise Model in Hospital Information Management Process Example." In Intelligent Systems for Sustainable Person-Centered Healthcare, 225–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79353-1_12.
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AbstractThe main purpose of this paper is to present knowledge-based Enterprise model (EM) sufficiency as data repository for Unified Modelling Language (UML) models generation. UML models are one of the most usable modelling languages in system lifecycle design stage, despite the problem domain of the system. UML models can be generated from Enterprise Model by using particular transformation algorithms presented in previous researches. Generation process from Enterprise model is represented by certain Hospital Information Management process example. Generated UML dynamic Use Case, Activity, Sequence and State models of different perspectives of Hospital Information Management process prove sufficiency of stored information in Enterprise model.
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Tran, Quang Duy, and Fabio Di Troia. "Word Embeddings for Fake Malware Generation." In Silicon Valley Cybersecurity Conference, 22–37. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_2.
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AbstractSignature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost $$90\%$$ of the time.
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Vázquez-Domínguez, Irene, and Alejandro Garanto. "Considerations for Generating Humanized Mouse Models to Test Efficacy of Antisense Oligonucleotides." In Methods in Molecular Biology, 267–79. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2010-6_18.
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AbstractOver the last decades, animal models have become increasingly important in therapeutic drug development and assessment. The use of these models, mainly mice and rats, allow evaluating drugs in the real-organism environment and context. However, several molecular therapeutic approaches are sequence-dependent, and therefore, the humanization of such models is required to assess the efficacy. The generation of genetically modified humanized mouse models is often an expensive and laborious process that may not always recapitulate the human molecular and/or physiological phenotype. In this chapter, we summarize basic aspects to consider before designing and generating humanized models, especially when they are aimed to test antisense-based therapies.
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Bian, Jiawen, and Xiaobo Zhou. "Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence." In Hidden Markov Models, 123–33. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7_9.
Full textConference papers on the topic "Generative sequence models"
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Shin, SungUk, Inseop Lee, and Changhee Choi. "Anomaly Dataset Augmentation Using the Sequence Generative Models." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00190.
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Zheng, Yanan, Lijie Wen, Jianmin Wang, Jun Yan, and Lei Ji. "Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3132952.
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Vychegzhanin, Sergey, Anastasia Kotelnikova, Alexander Sergeev, and Evgeny Kotelnikov. "MaxProb: Controllable Story Generation from Storyline." In INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-539-553.
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Controllable story generation towards keywords or key phrases is one of the purposes of using language models. Recent work has shown that various decoding strategies prove to be effective in achieving a high level of language control. Such strategies require less computational resources compared to approaches based on fine-tuning pre-trained language models. The paper proposes and investigates the method MaxProb of controllable story generation in Russian, which works at the decoding stage in the process of text generation. The method uses a generative language model to estimate the probability of its tokens in order to shift the content of the text towards the guide phrase. The idea of the method is to generate a set of different small sequences of tokens from the language model vocabulary, estimate the probability of following the guide phrase after each sequence, and choose the most probable sequence. The method allows evaluating the consistency of the token sequence for the transition from the prompt to the guide phrase. The study was carried out using the Russian-language corpus of stories with extracted events that make up the plot of the story. Experiments have shown the effectiveness of the proposed method for automatically creating stories from a set of plot phrases.
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Zhou, Shen, and Tieyun Qian. "On the Strength of Sequence Labeling and Generative Models for Aspect Sentiment Triplet Extraction." In Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.762.
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Li, Chen, Chikashige Yamanaka, Kazuma Kaitoh, and Yoshihiro Yamanishi. "Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/539.
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Deep generative models of sequence-structure data have attracted widespread attention in drug discovery. However, such models cannot fully extract the semantic features of molecules from sequential representations. Moreover, mode collapse reduces the diversity of the generated molecules. This paper proposes a transformer-based objective-reinforced generative adversarial network (TransORGAN) to generate molecules. TransORGAN leverages a transformer architecture as a generator and uses a stochastic policy gradient for reinforcement learning to generate plausible molecules with rich semantic features. The discriminator grants rewards that guide the policy update of the generator, while an objective-reinforced penalty encourages the generation of diverse molecules. Experiments were performed using the ZINC chemical dataset, and the results demonstrated the usefulness of TransORGAN in terms of uniqueness, novelty, and diversity of the generated molecules.
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Tao, Chongyang, Shen Gao, Mingyue Shang, Wei Wu, Dongyan Zhao, and Rui Yan. "Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/614.
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Attention mechanism has become a popular and widely used component in sequence-to-sequence models. However, previous research on neural generative dialogue systems always generates universal responses, and the attention distribution learned by the model always attends to the same semantic aspect. To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance. Further, a regularizer is formulated to force different attention heads to concentrate on certain aspects. The proposed mechanism leads to more informative, diverse, and relevant response generated. Experimental results show that our proposed model outperforms several strong baselines.
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Ye, Zhenhui, Zhou Zhao, Yi Ren, and Fei Wu. "SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/620.
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The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme sequence as input and thus cannot understand the tree-structured syntactic information of the input sequence, which hurts the prosody modeling. To this end, we propose SyntaSpeech, a syntax-aware and light-weight NAR-TTS model, which integrates tree-structured syntactic information into the prosody modeling modules in PortaSpeech. Specifically, 1) We build a syntactic graph based on the dependency tree of the input sentence, then process the text encoding with a syntactic graph encoder to extract the syntactic information. 2) We incorporate the extracted syntactic encoding with PortaSpeech to improve the prosody prediction. 3) We introduce a multi-length discriminator to replace the flow-based post-net in PortaSpeech, which simplifies the training pipeline and improves the inference speed, while keeping the naturalness of the generated audio. Experiments on three datasets not only show that the tree-structured syntactic information grants SyntaSpeech the ability to synthesize better audio with expressive prosody, but also demonstrate the generalization ability of SyntaSpeech to adapt to multiple languages and multi-speaker text-to-speech. Ablation studies demonstrate the necessity of each component in SyntaSpeech. Source code and audio samples are available at https://syntaspeech.github.io.
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Guo, Zhendong, Wei Sun, Liming Song, Jun Li, and Zhenping Feng. "Generative Transfer Optimization for Aerodynamic Design." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-225.
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Transfer optimization, one type of optimization methods, which leverages knowledge of the completed tasks to accelerate the design progress of a new task, has been in widespread use in machine learning community. However, when applying transfer optimization to accelerate the progress of aerodynamic shape optimization (ASO), two challenges are encountered in sequence, that is, (1) how to build a shared design space among the related aerodynamic design tasks, and (2) how to exchange information between tasks most efficiently. To address the first challenge, a datadriven generative model is used to learn airfoil representations from the existing database, with the aim of synthesizing various airfoil shapes in a shared design space. To address the second challenge, both singleand multifidelity Gaussian processes (GPs) are employed to carry out optimization. On one hand, the multifidelity GP is used to leverage knowledge from the completed tasks. On the other hand, mutual learning is established between singleand multifidelity GP models by exchanging information between them in each optimization cycle. With the above, a generative transfer optimization (GTO) framework is proposed to shorten the design cycle of aerodynamic design. Through airfoil optimizations at different working conditions, the effectiveness of the proposed GTO framework is demonstrated.
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Xiao, Dongling, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. "ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/553.
Full textAbstract:
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder. Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks, including abstractive summarization (Gigaword and CNN/DailyMail), question generation (SQuAD), dialogue generation (Persona-Chat) and generative question answering (CoQA). The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE/ernie-gen.
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Albuquerque, Isabela, Joao Monteiro, and Tiago Falk. "Generating Videos by Traversing Image Manifolds Learned by GANs." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812036.
Full textAbstract:
In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate synthetic scenes of moving objects. To do so, we propose a two-step training scheme within which: a generator of static frames is trained first. Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural. The adversarial setting is employed in both training steps. However, with the aim of avoiding known training instabilities in GANs, a multiple discriminator approach is used to train both models. Results in the studied video dataset indicate that, by employing such an approach, the recurrent part is able to learn how to coherently navigate the image manifold induced by the frames generator, thus yielding more natural-looking scenes.
Reports on the topic "Generative sequence models"
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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
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Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from the original cultivar. In the present project we focused on two of the most common date palm off-types: (1) trees with reduced fruit setting, in which most of the flowers turn into three-carpel parthenocarpic fruits. In a severe form, multi-carpel flowers and fruitlets (with up to six or eight carpels instead of the normal three-carpel flowers) are also formed. (2) dwarf trees, having fewer and shorter leaves, very short trunk and are not bearing fruits at their expected age, compared to the normal trees. Similar off-types occur in other crop species propagated by tissue culture, like banana (mainly dwarf plants) or oil palm (with a common 'Mantled' phenotype with reduced fruit setting and occurrence of supernumerary carpels). Some off-types can only be detected several years after planting in the fields. Therefore, efficient methods for prevention of the generation of off-types, as well as methods for their detection and early removal, are required for date palms, as well as for other tissue culture propagated crops. This research is aimed at the understanding of the mechanisms by which off-types are generated, and developing markers for their early identification. Several molecular and genomic approaches were applied. Using Methylation Sensitive AFLP and bisulfite sequencing, we detected changes in DNA methylation patterns occurring in off-types. We isolated and compared the sequence and expression of candidate genes, genes related to vegetative growth and dwarfism and genes related to flower development. While no sequence variation were detected, changes in gene expression, associated with the severity of the "fruit set" phenotype were detected in two genes - PdDEF (Ortholog of rice SPW1, and AP3 B type MADS box gene), and PdDIF (a defensin gene, highly homologous to the oil palm gene EGAD). We applied transcriptomic analyses, using high throughput sequencing, to identify genes differentially expressed in the "palm heart" (the apical meristem and the region of embryonic leaves) of dwarf vs. normal trees. Among the differentially expressed genes we identified genes related to hormonal biosynthesis, perception and regulation, genes related to cell expansion, and genes related to DNA methylation. Using Representation Difference Analyses, we detected changes in the genomes of off-type trees, mainly chloroplast-derived sequences that were incorporated in the nuclear genome and sequences of transposable elements. Sequences previously identified as differing between normal and off-type trees of oil palms or banana, successfully identified variation among date palm off-types, suggesting that these represent highly labile regions of monocot genomes. The data indicate that the date palm genome, similarly to genomes of other monocot crops as oil palm and banana, is quite unstable when cells pass through a cycle of tissue culture and regeneration. Changes in DNA sequences, translocation of DNA fragments and alteration of methylation patterns occur. Consequently, patterns of gene expression are changed, resulting in abnormal phenotypes. The data can be useful for future development of tools for early identification of off-type as well as for better understanding the phenomenon of somaclonal variation during propagation in vitro.
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Merkulova, Yuliya. Система цифровых моделей - новая технология для баланса данных. Yuliya Merkulova, April 2021. http://dx.doi.org/10.12731/er0430.26042021.
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Use of the digital technologies is new and very productive approach for balance of different data. It is very important for balance of supply and demand and for increase of competitiveness of products. Various types of digital models were developed as a result of scientific research, they found reflection in article. Digital models for the description of the list of the sequences of steps and operations of various stages and process in general allow to install system of interrelations between operations and steps and to reach necessary log-ic, increase of effectiveness of any process. Object-relational models for establishment of communications between data of various blocks of databases and functional models of the choice of strategy of data balance form analytical base for justification of the choice of the direction of transformation of data. Models of a combination of a plurality of various data of the offer of products in the form of matrixes of multi-purpose optimization have double effect, because they allow not only to develop various options of data combina-tion, taking into account opportunities of change of location of products over the markets and temporary phases, but also to estimate aggregate useful effect from products. These models together with models of comparison of various options and the choice of optimal solutions allow to generate compatible strategic and current programs of the offer of products as a plurality of the output data balanced with each other and with data of demand. It is providing the best synergetic result. The developed methodology of creation of system of the interconnected digital models for transformation of data and generation of the output data of the situational-strategic program of the offer of products is a cornerstone of formation of new digital econ-omy – of economy of balanced data.
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, May 2023. http://dx.doi.org/10.3289/sw_2_2023.
Full textAbstract:
The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.
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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.
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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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Michelmore, Richard, Eviatar Nevo, Abraham Korol, and Tzion Fahima. Genetic Diversity at Resistance Gene Clusters in Wild Populations of Lactuca. United States Department of Agriculture, February 2000. http://dx.doi.org/10.32747/2000.7573075.bard.
Full textAbstract:
Genetic resistance is often the least expensive, most effective, and ecologically-sound method of disease control. It is becoming apparent that plant genomes contain large numbers of disease resistance genes. However, the numbers of different resistance specificities within a genepool and the genetic mechanisms generating diversity are poorly understood. Our objectives were to characterize diversity in clusters of resistance genes in wild progenitors of cultivated lettuce in Israel and California in comparison to diversity within cultivated lettuce, and to determine the extent of gene flow, recombination, and genetic instability in generating variation within clusters of resistance genes. Genetic diversity of resistance genes was analyzed in wild and cultivated germplasm using molecular markers derived from lettuce resistance gene sequences of the NBS-LRR type that mapped to the major cluster if resistance genes in lettuce (Sicard et al. 1999). Three molecular markers, one microsatellite marker and two SCAR markers that amplified LRR- encoding regions, were developed from sequences of resistance gene homologs at the Dm3 cluster (RGC2s) in lettuce. Variation for these markers was assessed in germplasm including 74 genotypes of cultivated lettuce, L. saliva and 71 accessions of the three wild Lactuca spp., L. serriola, L. saligna and L. virosa that represent the major species in the sexually accessible genepool for lettuce. Diversity was also studied within and between natural populations of L. serriola from Israel and California. Large numbers of haplotypes were detected indicating the presence of numerous resistance genes in wild species. We documented a variety of genetic events occurring at clusters of resistance genes for the second objective (Sicard et al., 1999; Woo el al., in prep; Kuang et al., in prepb). The diversity of resistance genes in haplotypes provided evidence for gene duplication and unequal crossing over during the evolution of this cluster of resistance genes. Comparison of nine resistance genes in cv. Diana identified 22 gene conversion and five intergenic recombinations. We cloned and sequenced a 700 bp region from the middle of RGC2 genes from six genotypes, two each from L. saliva, L. serriola, and L. saligna . We have identified over 60 unique RGC2 sequences. Phylogenetic analysis surprisingly demonstrated much greater similarity between than within genotypes. This led to the realization that resistance genes are evolving much slower than had previously been assumed and to a new model as to how resistance genes are evolving (Michelmore and Meyers, 1998). The genetic structure of L. serriola was studied using 319 AFLP markers (Kuang et al., in prepa). Forty-one populations from Turkey, Armenia, Israel, and California as well as seven European countries were examined. AFLP marker data showed that the Turkish and Armenian populations were the most polymorphic populations and the European populations were the least. The Davis, CA population, a recent post-Columbian colonization, showed medium genetic diversity and was genetically close to the Turkish populations. Our results suggest that Turkey - Armenia may be the center of origin and diversity of L. serriola and may therefore have the greatest diversity of resistance genes. Our characterization of the diversity of resistance genes and the genetic mechanisms generating it will allow informed exploration, in situ and ex situ conservation, and utilization of germplasm resources for disease control. The results of this project provide the basis for our future research work, which will lead to a detailed understanding of the evolution of resistance genes in plants.
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Соловйов, Володимир Миколайович, Vladimir Saptsin, and Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, March 2009. http://dx.doi.org/10.31812/0564/1131.
Full textAbstract:
In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&P 500, RTS, PFTS and other.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Full textAbstract:
The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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Zhang, Hongbin B., David J. Bonfil, and Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586464.bard.
Full textAbstract:
The goals of this project were to develop essential genomic tools for modern chickpea genetics and genomics research, map the genes and quantitative traits of importance to chickpea production and generate DNA markers that are well-suited for enhanced chickpea germplasm analysis and breeding. To achieve these research goals, we proposed the following research objectives in this period of the project: 1) Develop an ordered BAC library with an average insert size of 150 - 200 kb (USA); 2) Develop 300 simple sequence repeat (SSR) markers with an aid of the BAC library (USA); 3) Develop SSR marker tags for Ascochyta response, flowering date and grain weight (USA); 4) Develop a molecular genetic map consisting of at least 200 SSR markers (Israel and USA); 5) Map genes and QTLs most important to chickpea production in the U.S. and Israel: Ascochyta response, flowering and seed set date, grain weight, and grain yield under extreme dryland conditions (Israel); and 6) Determine the genetic correlation between the above four traits (Israel). Chickpea is the third most important pulse crop in the world and ranks the first in the Middle East. Chickpea seeds are a good source of plant protein (12.4-31.5%) and carbohydrates (52.4-70.9%). Although it has been demonstrated in other major crops that the modern genetics and genomics research is essential to enhance our capacity for crop genetic improvement and breeding, little work was pursued in these research areas for chickpea. It was absent in resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. For instance, there were no large-insert BAC and BIBAC libraries, no sufficient and user- friendly DNA markers, and no intraspecific genetic map. Grain sizes, flowering time and Ascochyta response are three main constraints to chickpea production in drylands. Combination of large seeds, early flowering time and Ascochyta blight resistance is desirable and of significance for further genetic improvement of chickpea. However, it was unknown how many genes and/or loci contribute to each of the traits and what correlations occur among them, making breeders difficult to combine these desirable traits. In this period of the project, we developed the resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. In particular, we constructed the proposed large-insert BAC library and an additional plant-transformation-competent BIBAC library from an Israeli advanced chickpea cultivar, Hadas. The BAC library contains 30,720 clones and has an average insert size of 151 kb, equivalent to 6.3 x chickpea haploid genomes. The BIBAC library contains 18,432 clones and has an average insert size of 135 kb, equivalent to 3.4 x chickpea haploid genomes. The combined libraries contain 49,152 clones, equivalent to 10.7 x chickpea haploid genomes. We identified all SSR loci-containing clones from the chickpea BAC library, generated sequences for 536 SSR loci from a part of the SSR-containing BACs and developed 310 new SSR markers. From the new SSR markers and selected existing SSR markers, we developed a SSR marker-based molecular genetic map of the chickpea genome. The BAC and BIBAC libraries, SSR markers and the molecular genetic map have provided essential resources and tools for modern genetic and genomic analyses of the chickpea genome. Using the SSR markers and genetic map, we mapped the genes and loci for flowering time and Ascochyta responses; one major QTL and a few minor QTLs have been identified for Ascochyta response and one major QTL has been identified for flowering time. The genetic correlations between flowering time, grain weight and Ascochyta response have been established. These results have provided essential tools and knowledge for effective manipulation and enhanced breeding of the traits in chickpea.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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Gafni, Yedidya, and Vitaly Citovsky. Molecular interactions of TYLCV capsid protein during assembly of viral particles. United States Department of Agriculture, April 2007. http://dx.doi.org/10.32747/2007.7587233.bard.
Full textAbstract:
Tomato yellow leaf curl geminivirus (TYLCV) is a major pathogen of cultivated tomato, causing up to 100% crop loss in many parts of the world. The present proposal, a continuation of a BARD-funded project, expanded our understanding of the molecular mechanisms by which CP molecules, as well as its pre-coat partner V2, interact with each other (CP), with the viral genome, and with cellular proteins during assembly and movement of the infectious virions. Specifically, two major objectives were proposed: I. To study in detail the molecular interactions between CP molecules and between CP and ssDNA leading to assembly of infectious TYLCV virions. II. To study the roles of host cell factors in TYLCV assembly. Our research toward these goals has produced the following major achievements: • Characterization of the CP nuclear shuttling interactor, karyopherin alpha 1, its pattern of expression and the putative involvement of auxin in regulation of its expression. (#1 in our list of publication, Mizrachy, Dabush et al. 2004). • Identify a single amino acid in the capsid protein’s sequence that is critical for normal virus life-cycle. (#2 in our list of publications, Yaakov, Levy et al. in preparation). • Development of monoclonal antibodies with high specificity to the capsid protein of TYLCV. (#3 in our list of publications, Solmensky, Zrachya et al. in press). • Generation of Tomato plants resistant to TYLCV by expressing transgene coding for siRNA targeted at the TYLCV CP. (#4 in our list of publications, Zrachya, Kumar et al. in press). •These research findings provided significant insights into (i) the molecular interactions of TYLCV capsid protein with the host cell nuclear shuttling receptor, and (ii) the mechanism by which TYLCV V2 is involved in the silencing of PTGS and contributes to the virus pathogenicity effect. Furthermore, the obtained knowledge helped us to develop specific strategies to attenuate TYLCV infection, for example, by blocking viral entry into and/or exit out of the host cell nucleus via siRNA as we showed in our publication recently (# 4 in our list of publications). Finally, in addition to the study of TYLCV nuclear import and export, our research contributed to our understanding of general mechanisms for nucleocytoplasmic shuttling of proteins and nucleic acids in plant cells. Also integration for stable transformation of ssDNA mediated by our model pathogen Agrobacterium tumefaciens led to identification of plant specific proteins involved.