Academic literature on the topic 'Omic network inference'
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Journal articles on the topic "Omic network inference"
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Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (April 27, 2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
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Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates ‘continuous and categorical’ meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.
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Dohlman, Anders B., and Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference." Experimental Biology and Medicine 244, no. 6 (March 16, 2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.
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Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome’s role in disease, more precisely predict the microbiome’s response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease. Impact statement This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome’s role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.
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Ramos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov, and Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.
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Abstract Recent studies have demonstrated that, despite their nomenclature, gliomas recapitulate an interneuron progenitor-like state that drives tumor progression. During human neurodevelopment, interneurons arise from the subcortical ganglionic eminences and migrate tangentially into the neocortex, settling in the cortical plate where they integrate local neurocircuitry. Analogously, malignant glioblastoma (GBM) cells migrate from the tumor core into the surrounding healthy tissue. This innate infiltrative property renders these malignant cells elusive to surgical resection, leading to tumor recurrence. To understand the regulatory networks that drive tumor infiltration from a neurodevelopmental perspective, we generated a single-nucleus Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) dataset of 41,000 nuclei from the core and infiltrative edge of surgically resected GBM specimens (n = 4). Concurrently, we sequenced 46,000 nuclei from non-pathological, postmortem samples of second- and third-trimester neocortices (n = 17). We integrated these datasets with paired single-nucleus RNA sequencing (snRNA-seq) data and identified candidate regulatory TFs that exhibit high correlation between motif enrichment and TF expression. Using single-trajectory inference and pseudo-time analyses, we identified TCF12 as a potential driver of interneuron lineage fate in developing cortical progenitors. Given its implication in projection neuron migration, we were intrigued to find that TCF12 activity is highest in GBM cells with a migratory interneuron signature, hinting at its putative role in tumor infiltration. To understand the significance of these findings, we will interrogate other genes in the TCF12 regulatory network with the ultimate goal of identifying therapeutic targets that inhibit GBM infiltration.
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Grund, Eric M., A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.
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Abstract Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for pancreatic ductal adenocarcinoma (PDAC). PDAC is expected to be the second leading cause of cancer death and has a dismal 5 year survival rate of 10%. There is an urgent unmet need to detect the disease at an early stage and stratify patients into more effective treatment regimens within clinically meaningful timeframes. To accomplish this, robust quality controlled OMIC molecular profiling platforms and analytic solutions need to be deployed into precision medicine protocols to discover actionable biomarkers. Project Survival® is a multicenter (n=6), prospective biomarker study (NCT 02781012) of PDAC and relevant controls combining high-fidelity longitudinal phenotypic characterization, multi-omic profiling (proteomics, signaling lipidomics, structural lipidomics, and metabolomics), and agnostic Bayesian artificial intelligence network inference (bAIcis®) to discover biomarkers with diagnostic and therapeutic utility. This study utilizes a systems medicine approach for translational biomarker discovery by performing analysis of matched subject sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissues and integrating them with the respective full clinical annotation using the BERG Interrogative Biology® platform. Multiple longitudinal time points were taken over the course of the six-year timeline enabling dynamic modeling. Utilizing the Project Survival® molecular and clinical data, we have analyzed and integrated baseline samples from 121 at risk patients and 279 patients with PDAC. Samples were randomized and analyzed over the course of recruitment allowing for agnostic discovery and integration to determine diagnostic utility. Discovery analysis identified 123 potential molecular markers, of which, four demonstrated a combined AUC of 0.85, PPV 0.83, NPV 0.72, and OR 13.1. In parallel, 4 non-canonical clinical measurements were assessed for diagnostic utility providing an AUC 0.79, PPV 0.84, NPV 0.72 and OR 13.2. Combining molecular and clinical features demonstrated an AUC of 0.9, PPV 0.9, NPV 0.77, OR 29.2, and p-value 1.4 E-40. Molecular markers revealed no treatment associated expression effects. Taken together, these marker panels demonstrate diagnostic utility to detect PDAC and will be further validated using robust bioanalysis methods as well as in an independent cohort of samples to provide enhanced insight into their positioning in the diagnostic landscape for PDAC. Citation Format: Eric M. Grund, A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, Valarie Bussberg, Vladimir Tolstikov, Rangaprasad Sarangarajan, Elder Granger, Niven Narian, Michael A. Kiebish. Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5145.
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Nathasingh, Brandon, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church, and Yaoyu E. Wang. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model." Cancer Research 83, no. 8_Supplement (April 14, 2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.
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Abstract Recent advances in artificial intelligence (AI) and availability of multimodal patient datasets have enabled the construction of complex network models to derive disease molecular mechanisms and predict the impact of therapeutic intervention. However, observational datasets are commonly affected by confounding factors making causal interpretation challenging. Causal inference network methods are particularly suited to facilitate therapeutic intervention studies by inferring the causal structure from sufficiently detailed multi-omic molecular data. The learned models enable in-silico loss-of-function screening experiments on patient data by using counterfactual simulation to reveal the impact of a gene loss in a disease model. These models enhance gene dependency characterization and the design of advanced therapeutic interventions. In this study, we developed an in-silico multiple myeloma (MM) patient causal model of overall survival (OS) based on transcriptomic expression, clinical, and genomic alteration data from Multiple Myeloma Research Foundation (MMRF) CoMMpass dataset (IA19). After filtering for data quality and availability, we included 516 patients, with 60% being hyperdiploid. We sampled Bayesian networks using Markov Chain Monte Carlo simulation to infer probabilistic causal relationships between network components that influence overall survival. We then simulated synthetic knock downs of those genes where a path exists to overall survival with posterior probability of at least 0.25. Next, we compared CRISPR-SpCas9 cancer dependency data from DepMap (version 22Q2) for multiple myeloma cell lines against genes predicted to be causal (causal genes) for overall survival. Last, we examined the causal genes that are non-dependent in MM cell lines for upstream genomic alterations to determine if specific patient genomic contexts are affecting the results. We identified 102 causal genes, including non-coding RNA genes (n=23), driving overall survival. Among them, 70% (56/79, p=2.2e-16, OR=9.5) of the coding genes were found to be MM-dependent in DepMap, with 44 common essential, 7 strongly selective and 5 weakly selective genes. From 23 genes identified as causal and not known to be MM-dependent, 20 (87%) were selective in other cancer lineages and all of them (23/23) had consistent upstream genomic alterations driving their expression. Causal genes identified from AI-driven in-silico experiments to predict overall survival were strongly enriched for known dependent genes from DepMap. Furthermore, we identified causal genes that may be dependent in unique patient genomic contexts. This demonstrates in-silico AI causal modelling is a powerful tool for exploring cancer cell vulnerability directly from patient data to advance target discovery. Citation Format: Brandon Nathasingh, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church, Yaoyu E. Wang. Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB181.
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Ye, Qing, and Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets." Cells 12, no. 1 (December 26, 2022): 101. http://dx.doi.org/10.3390/cells12010101.
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There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Alanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, Claire S. Simon, Afshan McCarthy, Kay Elder, Phil Snell, Leila Christie, and Kathy K. Niakan. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos." Life Science Alliance 7, no. 1 (October 25, 2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.
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Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.
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Wang, Pei. "Network biology: Recent advances and challenges." Gene & Protein in Disease 1, no. 2 (October 6, 2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.
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Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information mining in biological networks are reviewed. The principles of guilt-by-association and guilt-by-rewiring in network biology and their applications are discussed. Although great advances have been achieved in the field of network biology over the past decades, there are still many challenging issues. First, efficient and reliable network inference algorithms for high-dimensional and highly noisy omics data are still in great demand. Second, the research focus will be on multilayer biological network theory. This plays a critical role in the exploration of the multi-scale or dynamical characteristics of complex biomolecular networks by integrating multi-source heterogeneous omics data. Third, a close cooperation among biologists, medical workers, and researchers from network science is still a prerequisite in the applications of network biology. The rapid development of network biology will undoubtedly raise important clues for understanding complex phenotypes in biological systems.
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Yan, Yan, Feng Jiang, Xinan Zhang, and Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm." Entropy 24, no. 5 (May 13, 2022): 693. http://dx.doi.org/10.3390/e24050693.
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One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.
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Bonnet, Eric, Laurence Calzone, and Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree." PLOS Computational Biology 11, no. 2 (February 13, 2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.
Full textDissertations / Theses on the topic "Omic network inference"
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Arsenteva, Polina. "Statistical modeling and analysis of radio-induced adverse effects based on in vitro and in vivo data." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCK074.
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Dans ce travail nous abordons le problème des effets indésirables induits par la radiothérapie sur les tissus sains. L'objectif est de proposer un cadre mathématique pour comparer les effets de différentes modalités d'irradiation, afin de pouvoir éventuellement choisir les traitements qui produisent le moins d'effets indésirables pour l’utilisation potentielle en clinique. Les effets secondaires sont étudiés dans le cadre de deux types de données : en termes de réponse omique in vitro des cellules endothéliales humaines, et en termes d'effets indésirables observés sur des souris dans le cadre d'expérimentations in vivo. Dans le cadre in vitro, nous rencontrons le problème de l'extraction d'informations clés à partir de données temporelles complexes qui ne peuvent pas être traitées avec les méthodes disponibles dans la littérature. Nous modélisons le fold change radio-induit, l'objet qui code la différence d'effet de deux conditions expérimentales, d’une manière qui permet de prendre en compte les incertitudes des mesures ainsi que les corrélations entre les entités observées. Nous construisons une distance, avec une généralisation ultérieure à une mesure de dissimilarité, permettant de comparer les fold changes en termes de toutes leurs propriétés statistiques importantes. Enfin, nous proposons un algorithme computationnellement efficace effectuant le clustering joint avec l'alignement temporel des fold changes. Les caractéristiques clés extraites de ces dernières sont visualisées à l'aide de deux types de représentations de réseau, dans le but de faciliter l'interprétation biologique. Dans le cadre in vivo, l’enjeu statistique est d’établir un lien prédictif entre des variables qui, en raison des spécificités du design expérimental, ne pourront jamais être observées sur les mêmes animaux. Dans le contexte de ne pas avoir accès aux lois jointes, nous exploitons les informations supplémentaires sur les groupes observés pour déduire le modèle de régression linéaire. Nous proposons deux estimateurs des paramètres de régression, l'un basé sur la méthode des moments et l'autre basé sur le transport optimal, ainsi que des estimateurs des intervalles de confiance basés sur le bootstrap stratifiéIn this work we address the problem of adverse effects induced by radiotherapy on healthy tissues. The goal is to propose a mathematical framework to compare the effects of different irradiation modalities, to be able to ultimately choose those treatments that produce the minimal amounts of adverse effects for potential use in the clinical setting. The adverse effects are studied in the context of two types of data: in terms of the in vitro omic response of human endothelial cells, and in terms of the adverse effects observed on mice in the framework of in vivo experiments. In the in vitro setting, we encounter the problem of extracting key information from complex temporal data that cannot be treated with the methods available in literature. We model the radio-induced fold change, the object that encodes the difference in the effect of two experimental conditions, in the way that allows to take into account the uncertainties of measurements as well as the correlations between the observed entities. We construct a distance, with a further generalization to a dissimilarity measure, allowing to compare the fold changes in terms of all the important statistical properties. Finally, we propose a computationally efficient algorithm performing clustering jointly with temporal alignment of the fold changes. The key features extracted through the latter are visualized using two types of network representations, for the purpose of facilitating biological interpretation. In the in vivo setting, the statistical challenge is to establish a predictive link between variables that, due to the specificities of the experimental design, can never be observed on the same animals. In the context of not having access to joint distributions, we leverage the additional information on the observed groups to infer the linear regression model. We propose two estimators of the regression parameters, one based on the method of moments and the other based on optimal transport, as well as the estimators for the confidence intervals based on the stratified bootstrap procedure
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Wrzodek, Clemens [Verfasser]. "Inference and integration of biochemical networks with multilayered omics data / Clemens Wrzodek." München : Verlag Dr. Hut, 2013. http://d-nb.info/1042307652/34.
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Vincent, Jonathan. "Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre." Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22485/document.
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La teneur et la composition en protéines de réserve du grain de blé tendre (Triticum aestivum L.) sont les principaux déterminants de sa valeur d’usage et de sa qualité nutritionnelle. La composition en protéines de réserve du grain est déterminée par la teneur en assimilâts azotés et soufrés par grain via des lois d’échelle qui pourraient être les propriétés émergentes de réseaux de régulation. Plusieurs facteurs de transcription intervenant dans cette régulation ont été mis en évidence, mais les voies et mécanismes impliqués sont encore très peu connus. Le constat est identique en ce qui concerne l’impact de la nutrition azotée et soufrée sur ce réseau de régulation. Le développement des outils de génomique fonctionnelle et de bioinformatique permet aujourd’hui d’aborder ces régulations de manière globale via une approche systémique mettant en relation plusieurs niveaux de régulation. L’objectif du travail présenté est d’explorer les réseaux de régulation –omiques impliqués dans le contrôle de l’accumulation des protéines de réserve dans le grain de blé tendre et leur réponse à l’approvisionnement en azote et en soufre. Une approche d’inférence de réseaux basée sur la découverte de règles a été étendue, implémentée sous la forme d’une plateforme web. L’utilisation de cette plateforme a permis de définir des sémantiques multiples afin d’inférer dans un cadre global, des règles possédant différentes significations biologiques. Des facteurs de transcription spécifiques de certains organes et certaines phases de développement ont été mis en évidence et un intérêt particulier a été apporté à leur position dans les réseaux de règles inférés, notamment en relation avec les protéines de réserve. Les travaux initiés dans cette thèse ouvrent un champ d’investigation innovant pour l’identification de nouvelles cibles de sélection variétale pour l’amélioration de la valeur technologique et de la qualité nutritionnelle du blé. Ils devraient ainsi permettre de mieux maîtriser la composition en protéines de réserve et ainsi produire des blés adaptés à des utilisations ciblées ou carencé en certaines fractions protéiques impliquées dans des phénomènes d’allergénicité et d’intolérance du gluten, ce dans un contexte d’agriculture durable et plus économe en intrantsGrain storage protein content and composition are the main determinants of bread wheat (Triticum aestivum L.) end-use value. Scaling laws governing grain protein composition according to grain nitrogen and sulfur content could be the outcome of a finely tuned regulation network. Although it was demonstrated that the main regulation of grain storage proteins accumulation occurs at the transcriptomic level in cereals, knowledge of the underlying molecular mechanisms is elusive. Moreover, the effects of nitrogen and sulfur on these mechanisms are unknown. The issue of skyrocketing data generation in research projects is addressed by developing high-throughput bioinformatics approaches. Extracting knowledge on from such massive amounts of data is therefore an important challenge. The work presented herein aims at elucidating regulatory networks involved in grain storage protein synthesis and their response to nitrogen and sulfur supply using a rule discovery approach. This approach was extended, implemented in the form of a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data. This platform allowed us to define different semantics in a comprehensive framework; each semantic having its own biological meaning, thus providing us with global informative networks. Spatiotemporal specificity of transcription factors expression was observed and particular attention was paid to their relationship with grain storage proteins in the inferred networks. The work initiated here opens up a field of innovative investigation to identify new targets for plant breeding and for an improved end-use value and nutritional quality of wheat in the context of inputs limitation. Further analyses should enhance the understanding of the control of grain protein composition and allow providing wheat adapted to specific uses or deficient in protein fractions responsible for gluten allergenicity and intolerance
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Hulot, Audrey. "Analyses de données omiques : clustering et inférence de réseaux Female ponderal index at birth and idiopathic infertility." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL034.
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Le développement des méthodes de biologie haut-débit (séquençage et spectrométrie de masse) a permis de générer de grandes masses de données, dites -omiques, qui nous aident à mieux comprendre les processus biologiques.Cependant, isolément, chaque source -omique ne permet d'expliquer que partiellement ces processus. Mettre en relation les différentes sources de donnés -omiques devrait permettre de mieux comprendre les processus biologiques mais constitue un défi considérable.Dans cette thèse, nous nous intéressons particulièrement aux méthodes de clustering et d’inférence de réseaux, appliquées aux données -omiques.La première partie du manuscrit présente trois méthodes. Les deux premières méthodes sont applicables dans un contexte où les données peuvent être de nature hétérogène.La première concerne un algorithme d’agrégation d’arbres, permettant la construction d’un clustering hiérarchique consensus. La complexité sous-quadratique de cette méthode a fait l’objet d’une démonstration, et permet son application dans un contexte de grande dimension. Cette méthode est disponible dans le package R mergeTrees, accessible sur le CRAN.La seconde méthode concerne l’intégration de données provenant d’arbres ou de réseaux, en transformant les objets via la distance cophénétique ou via le plus court chemin, en matrices de distances. Elle utilise le Multidimensional Scaling et l’Analyse Factorielle Multiple et peut servir à la construction d’arbres et de réseaux consensus.Enfin, dans une troisième méthode, on se place dans le contexte des modèles graphiques gaussiens, et cherchons à estimer un graphe, ainsi que des communautés d’entités, à partir de plusieurs tables de données. Cette méthode est basée sur la combinaison d’un Stochastic Block Model, un Latent block Model et du Graphical Lasso.Cette thèse présente en deuxième partie les résultats d’une étude de données transcriptomiques et métagénomiques, réalisée dans le cadre d’un projet appliqué, sur des données concernant la Spondylarthrite ankylosanteThe development of biological high-throughput technologies (next-generation sequencing and mass spectrometry) have provided researchers with a large amount of data, also known as -omics, that help better understand the biological processes.However, each source of data separately explains only a very small part of a given process. Linking the differents -omics sources between them should help us understand more of these processes.In this manuscript, we will focus on two approaches, clustering and network inference, applied to omics data.The first part of the manuscript presents three methodological developments on this topic. The first two methods are applicable in a situation where the data are heterogeneous.The first method is an algorithm for aggregating trees, in order to create a consensus out of a set of trees. The complexity of the process is sub-quadratic, allowing to use it on data leading to a great number of leaves in the trees. This algorithm is available in an R-package named mergeTrees on the CRAN.The second method deals with the integration data from trees and networks, by transforming these objects into distance matrices using cophenetic and shortest path distances, respectively. This method relies on Multidimensional Scaling and Multiple Factor Analysis and can be also used to build consensus trees or networks.Finally, we use the Gaussian Graphical Models setting and seek to estimate a graph, as well as communities in the graph, from several tables. This method is based on a combination of Stochastic Block Model, Latent Block Model and Graphical Lasso.The second part of the manuscript presents analyses conducted on transcriptomics and metagenomics data to identify targets to gain insight into the predisposition of Ankylosing Spondylitis
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Griffin, Paula Jean. "Biological network models for inferring mechanism of action, characterizing cellular phenotypes, and predicting drug response." Thesis, 2016. https://hdl.handle.net/2144/14516.
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A primary challenge in the analysis of high-throughput biological data is the abundance of correlated variables. A small change to a gene's expression or a protein's binding availability can cause significant downstream effects. The existence of such chain reactions presents challenges in numerous areas of analysis. By leveraging knowledge of the network interactions that underlie this type of data, we can often enable better understanding of biological phenomena. This dissertation will examine network-based statistical approaches to the problems of mechanism-of-action inference, characterization of gene expression changes, and prediction of drug response.
First, we develop a method for multi-target perturbation detection in multi-omics biological data. We estimate a joint Gaussian graphical model across multiple data types using penalized regression, and filter for network effects. Next, we apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. We also present a conditional testing procedure to allow for detection of secondary perturbations.
Second, we address the problem of characterization of cellular phenotypes via Bayesian regression in the Gene Ontology (GO). In our model, we use the structure of the GO to assign changes in gene expression to functional groups, and to model the covariance between these groups. In addition to describing changes in expression, we use these functional activity estimates to predict the expression of unobserved genes. We further determine when such predictions are likely to be inaccurate by identifying GO terms with poor agreement to gene-level estimates. In a case study, we identify GO terms relevant to changes in the growth rate of S. cerevisiae.
Lastly, we consider the prediction of drug sensitivity in cancer cell lines based on pathway-level activity estimates from ASSIGN, a Bayesian factor analysis model. We use penalized regression to predict response to various cancer treatments based on cancer subtype, pathway activity, and 2-way interactions thereof. We also present network representations of these interaction models and examine common patterns in their structure across treatments.
Book chapters on the topic "Omic network inference"
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Lecca, Paola, Thanh-Phuong Nguyen, Corrado Priami, and Paola Quaglia. "Network Inference from Time-Dependent Omics Data." In Methods in Molecular Biology, 435–55. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_20.
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Coletti, Roberta, and Marta B. Lopes. "Multi-omics Data Integration and Network Inference for Biomarker Discovery in Glioma." In Progress in Artificial Intelligence, 247–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49011-8_20.
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Balagué, Natàlia, Sergi Garcia-Retortillo, Robert Hristovski, and Plamen Ch. Ivanov. "From Exercise Physiology to Network Physiology of Exercise." In Exercise Physiology [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.102756.
Full textAbstract:
Exercise physiology (EP) and its main research directions, strongly influenced by reductionism from its origins, have progressively evolved toward Biochemistry, Molecular Biology, Genetics, and OMICS technologies. Although these technologies may be based on dynamic approaches, the dominant research methodology in EP, and recent specialties such as Molecular Exercise Physiology and Integrative Exercise Physiology, keep focused on non-dynamical bottom-up statistical inference techniques. Inspired by the new field of Network Physiology and Complex Systems Science, Network Physiology of Exercise emerges to transform the theoretical assumptions, the research program, and the practical applications of EP, with relevant consequences on health status, exercise, and sport performance. Through an interdisciplinary work with diverse disciplines such as bioinformatics, data science, applied mathematics, statistical physics, complex systems science, and nonlinear dynamics, Network Physiology of Exercise focuses the research efforts on improving the understanding of different exercise-related phenomena studying the nested dynamics of the vertical and horizontal physiological network interactions. After reviewing the EP evolution during the last decades and discussing their main theoretical and methodological limitations from the lens of Complex Networks Science, we explain the potential impact of the emerging field of Network Physiology of Exercise and the most relevant data analysis techniques and evaluation tools used until now.
Conference papers on the topic "Omic network inference"
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Zarayeneh, Neda, Jung Hun Oh, Donghyun Kim, Chunyu Liu, Jean Gao, Sang C. Suh, and Mingon Kang. "Integrative Gene Regulatory Network inference using multi-omics data." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822711.
Full textReports on the topic "Omic network inference"
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Richardson, Ruth. Systems Biology of Dehalococcoides: Using Network Inference Modeling to Integrate Omics Datasets Under Varied Conditions. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada559471.
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