Relevant bibliographies by topics / Gene network reconstruction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Gene network reconstruction'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Gene network reconstruction"

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Bhola, Abhishek, and Sandeep Mittal. "Reconstruction of Gene Regulatory Network using Bayesian Network." IOP Conference Series: Materials Science and Engineering 1042, no. 1 (January 1, 2021): 012009. http://dx.doi.org/10.1088/1757-899x/1042/1/012009.

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Soinov, L. A. "Supervised classification for gene network reconstruction." Biochemical Society Transactions 31, no. 6 (December 1, 2003): 1497–502. http://dx.doi.org/10.1042/bst0311497.

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One of the central problems of functional genomics is revealing gene expression networks – the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers – functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.
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Orlov, Y. L., A. G. Galieva, N. G. Orlova, E. N. Ivanova, Y. A. Mozyleva, and A. A. Anashkina. "Reconstruction of gene network associated with Parkinson disease for gene targets search." Biomeditsinskaya Khimiya 67, no. 3 (2021): 222–30. http://dx.doi.org/10.18097/pbmc20216703222.

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Accumulation of genetic data in the field of Parkinson's disease research culminated in identifying risk factors and confident prediction of the disease occurrence. To find new gene-targets for diagnostics and therapy we have to reconstruct gene network of the disease, to cluster genes in the network, to reveal key (hub) genes with largest number of interactions in the network. Using the on-line bioinformatics tools OMIM, PANTHER, g:Profiler, GeneMANIA, and STRING-DB, we have analyzed the current array of data related to Parkinson's disease, calculated the categories of gene ontologies for a large list of genes, visualized them, and built gene networks containing the identified key objects and their relationships. However, translating the results into biological understanding is still a promising major challenge. The analysis of the genes associated with the disease, the assessment of their place in the gene network (connectivity) allows us to evaluate them as target genes for medicinal effects.
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Manshaei, Roozbeh, Pooya Sobhe Bidari, Mahdi Aliyari Shoorehdeli, Amir Feizi, Tahmineh Lohrasebi, Mohammad Ali Malboobi, Matthew Kyan, and Javad Alirezaie. "Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction." ISRN Bioinformatics 2012 (November 1, 2012): 1–16. http://dx.doi.org/10.5402/2012/419419.

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Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.
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Bansal, Bhavana, Aparajita Nanda, and Anita Sahoo. "Intelligent Framework With Controlled Behavior for Gene Regulatory Network Reconstruction." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijirr.2022010104.

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Gene Regulatory Networks (GRNs) are the pioneering methodology for finding new gene interactions getting insights of the biological processes using time series gene expression data. It remains a challenge to study the temporal nature of gene expression data that mimic complex non-linear dynamics of the network. In this paper, an intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data. A novel PSO algorithm enhanced by human cognition influenced by the ideology of Bhagavad Gita is employed for improved learning of RNN. RNN guided by the proposed algorithm simulates the nonlinear and dynamic gene interactions to a greater extent. The proposed method shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks. The strength of the method is verified by analyzing the small artificial network and real data of Escherichia coli with improved accuracy.
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ANDRECUT, M., S. A. KAUFFMAN, and A. M. MADNI. "EVIDENCE OF SCALE-FREE TOPOLOGY IN GENE REGULATORY NETWORK OF HUMAN TISSUES." International Journal of Modern Physics C 19, no. 02 (February 2008): 283–90. http://dx.doi.org/10.1142/s0129183108012091.

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We report the reconstruction of the topology of gene regulatory network in human tissues. The results show that the connectivity of the regulatory gene network is characterized by a scale-free distribution. This result supports the hypothesis that scale-free networks may represent the common blueprint for gene regulatory networks.
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Thompson, Dawn, Aviv Regev, and Sushmita Roy. "Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution." Annual Review of Cell and Developmental Biology 31, no. 1 (November 13, 2015): 399–428. http://dx.doi.org/10.1146/annurev-cellbio-100913-012908.

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Bhyratae, Suhas A. "Reconstruction of Gene Regulatory Network for Colon Cancer Dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3711–16. http://dx.doi.org/10.22214/ijraset.2022.45879.

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Abstract: Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major functions of living cells. DNA microarray probes how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. Microarray is a technology that has been widely used to probe the presence of genes in a sample of DNA or RNA. This technology helps to check the expression levels of thousands of genes together. The DNA microarray was established as a tool for the efficient collection of mRNA expression for a large number of genes. The mapping function route maps pairs of genes that present similar positive, and negative interactions and also defines how the range of each gene is going to be segmented. From all the combinations a function transforms each pair of labels into another one that classifies the type of interaction. This project addresses the challenge of reconstructing molecular networks and gene regulation from gene expression data. Reconstruction of gene regulatory networks which can also be called reverse engineering is a process of identifying gene interaction networks from the experimental microarray gene expression profiles through computation techniques. The main features involved in the computation of interaction in the filtered genes are the discretization mapping function, gene-gene mapping function, and filtering function.
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Di Filippo, Marzia, Chiara Damiani, and Dario Pescini. "GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction." PLOS Computational Biology 17, no. 11 (November 8, 2021): e1009550. http://dx.doi.org/10.1371/journal.pcbi.1009550.

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Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.
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Leday, Gwenaël G. R., Mathisca C. M. de Gunst, Gino B. Kpogbezan, Aad W. van der Vaart, Wessel N. van Wieringen, and Mark A. van de Wiel. "Gene network reconstruction using global-local shrinkage priors." Annals of Applied Statistics 11, no. 1 (March 2017): 41–68. http://dx.doi.org/10.1214/16-aoas990.

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Dissertations / Theses on the topic "Gene network reconstruction"

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ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.

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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian networks is proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data methods's comparison was carried out for networks of increasing dimension, for measurements taken at different time granularity densities and for measurements evenly vs. unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network dimensions. Furthermore, their performance degraded smoothly as the dimension of the network increased. Continuous time Bayesian network were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms as well as new plausible biological insights. Continuous time Bayesian networks resulted to be effective on networks of both small and big dimensions and particularly feasible when the measurements are not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops suggesting that Th17 cells may be auto regulating their own differentiation process.
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Fichtenholtz, Alexander Michael. "In silico bacterial gene regulatory network reconstruction from sequence." Thesis, Boston University, 2012. https://hdl.handle.net/2144/32880.

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Thesis (Ph.D.)--Boston University
PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
DNA sequencing techniques have evolved to the point where one can sequence millions of bases per minute, while our capacity to use this information has been left behind. One particularly notorious example is in the area of gene regulatory networks. A molecular study of gene regulation proceeds one protein at a time, requiring bench scientists months of work purifying transcription factors and performing DNA footprinting studies. Massive scale options like ChIP-Seq and microarrays are a step up, but still require considerable resources in terms of manpower and materials. While computational biologists have developed methods to predict protein function from sequence, gene locations from sequence, and even metabolic networks from sequence, the space of regulatory network reconstruction from sequence remains virtually untouched. Part of the reason comes from the fact that the components of a regulatory interaction, such as transcription factors and binding sites, are difficult to detect. The other, more prominent reason, is that there exists no "recognition code" to determine which transcription factors will bind which sites. I've created a pipeline to reconstruct regulatory networks starting from an unannotated complete genomic sequence for a prokaryotic organism. The pipeline predicts necessary information, such as gene locations and transcription factor sequences, using custom tools and third party software. The core step is to determine the likelihood of interaction between a TF and a binding site using a black box style recognition code developed by applying machine learning methods to databases of prokaryotic regulatory interactions. I show how one can use this pipeline to reconstruct the virtually unknown regulatory network of Bacillus anthracis.
2031-01-01
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Li, Song. "Integrate qualitative biological knowledge for gene regulatory network reconstruction with dynamic Bayesian networks." [Ames, Iowa : Iowa State University], 2007.

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Steiger, Edgar [Verfasser]. "Efficient Sparse-Group Bayesian Feature Selection for Gene Network Reconstruction / Edgar Steiger." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1170876633/34.

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Kröger, Stefan. "Bioinformatic analyses for T helper cell subtypes discrimination and gene regulatory network reconstruction." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18122.

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Die Etablierung von Hochdurchsatz-Technologien zur Durchführung von Genexpressionsmessungen führte in den letzten 20 Jahren zu einer stetig wachsende Menge an verfügbaren Daten. Sie ermöglichen durch Kombination einzelner Experimente neue Vergleichsstudien zu kombinieren oder Experimente aus verschiedenen Studien zu großen Datensätzen zu vereinen. Dieses Vorgehen wird als Meta-Analyse bezeichnet und in dieser Arbeit verwendet, um einen großen Genexpressionsdatensatz aus öffentlich zugänglichen T-Zell Experimenten zu erstellen. T-Zellen sind Immunzellen, die eine Vielzahl von unterschiedlichen Funktionen des Immunsystems inititiieren und steuern. Sie können in verschiedene Subtypen mit unterschiedlichen Funktionen differenzieren. Der mittels Meta-Analyse erstellte Datensatz beinhaltet nur Experimente zu einem T-Zell-Subtyp, den regulatorischen T-Zellen (Treg) bzw. der beiden Untergruppen, natürliche Treg (nTreg) und induzierte Treg (iTreg) Zellen. Eine bisher unbeantwortete Frage lautet, welche subtyp-spezifischen gen-regulatorische Mechanismen die T-Zell Differenzierung steuern. Dazu werden in dieser Arbeit zwei spezifische Herausforderungen der Treg Forschung behandelt: (i) die Identifikation von Zelloberflächenmarkern zur Unterscheidung und Charakterisierung der Subtypen, sowie (ii) die Rekonstruktion von Treg-Zell-spezifischen gen-regulatorischen Netzwerken (GRN), die die Differenzierungsmechanismen beschreiben. Die implementierte Meta-Analyse kombiniert mehr als 150 Microarray-Experimente aus über 30 Studien in einem Datensatz. Dieser wird benutzt, um mittels Machine Learning Zell-spezifische Oberflächenmarker an Hand ihres Expressionsprofils zu identifizieren. Mit der in dieser Arbeit entwickelten Methode wurden 41 Genen extrahiert, von denen sechs Oberflächenmarker sind. Zusätzliche Validierungsexperimente zeigten, dass diese sechs Gene die Experimenten beider T-Zell Subtypen sicher unterscheiden können. Zur Rekonstruktion von GRNs vergleichen wir unter Verwendung des erstellten Datensatzes 11 verschiedene Algorithmen und evaluieren die Ergebnisse mit Informationen aus Interaktionsdatenbanken. Die Evaluierung zeigt, dass die derzeit verfügbaren Methoden nicht in der Lage sind den Wissensstand Treg-spezifischer, regulatorsicher Mechanismen zu erweitern. Abschließend präsentieren wir eine Datenintegrationstrategie zur Rekonstruktion von GRN am Beispiel von Th2 Zellen. Aus Hochdurchsatzexperimenten wird ein Th2-spezifisches GRN bestehend aus 100 Genen rekonstruiert. Während 89 dieser Gene im Kontext der Th2-Zelldifferenzierung bekannt sind, wurden 11 neue Kandidatengene ohne bisherige Assoziation zur Th2-Differenzierung ermittelt. Die Ergebnisse zeigen, dass Datenintegration prinzipiell die GRN Rekonstruktion ermöglicht. Mit der Verfügbarkeit von mehr Daten mit besserer Qualität ist zu erwarten, dass Methoden zur Rekonstruktion maßgeblich zum besseren Verstehen der zellulären Differenzierung im Immunsystem und darüber hinaus beitragen können und so letztlich die Ursachenforschung von Dysfunktionen und Krankheiten des Immunsystems ermöglichen werden.
Within the last two decades high-throughput gene expression screening technologies have led to a rapid accumulation of experimental data. The amounts of information available have enabled researchers to contrast and combine multiple experiments by synthesis, one of such approaches is called meta-analysis. In this thesis, we build a large gene expression data set based on publicly available studies for further research on T cell subtype discrimination and the reconstruction of T cell specific gene regulatory events. T cells are immune cells which have the ability to differentiate into subtypes with distinct functions, initiating and contributing to a variety of immune processes. To date, an unsolved problem in understanding the immune system is how T cells obtain a specific subtype differentiation program, which relates to subtype-specific gene regulatory mechanisms. We present an assembled expression data set which describes a specific T cell subset, regulatory T (Treg) cells, which can be further categorized into natural Treg (nTreg) and induced Treg (iTreg) cells. In our analysis we have addressed specific challenges in regulatory T cell research: (i) discriminating between different Treg cell subtypes for characterization and functional analysis, and (ii) reconstructing T cell subtype specific gene regulatory mechanisms which determine the differences in subtype-specific roles for the immune system. Our meta-analysis strategy combines more than one hundred microarray experiments. This data set is applied to a machine learning based strategy of extracting surface protein markers to enable Treg cell subtype discrimination. We identified a set of 41 genes which distinguish between nTregs and iTregs based on gene expression profile only. Evaluation of six of these genes confirmed their discriminative power which indicates that our approach is suitable to extract candidates for robust discrimination between experiment classes. Next, we identify gene regulatory interactions using existing reconstruction algorithms aiming to extend the number of known gene-gene interactions for Treg cells. We applied eleven GRN reconstruction tools based on expression data only and compared their performance. Taken together, our results suggest that the available methods are not yet sufficient to extend the current knowledge by inferring so far unreported Treg specific interactions. Finally, we present an approach of integrating multiple data sets based on different high-throughput technologies to reconstruct a subtype-specific GRN. We constructed a Th2 cell specific gene regulatory network of 100 genes. While 89 of these are known to be related to Th2 cell differentiation, we were able to attribute 11 new candidate genes with a function in Th2 cell differentiation. We show that our approach to data integration does, in principle, allow for the reconstruction of a complex network. Future availability of more and more consistent data may enable the use of the concept of GRN reconstruction to improve understanding causes and mechanisms of cellular differentiation in the immune system and beyond and, ultimately, their dysfunctions and diseases.
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Chen, Wei, and 陈玮. "A factor analysis approach to transcription regulatory network reconstruction using gene expression data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49617783.

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Reconstruction of Transcription Regulatory Network (TRN) and Transcription Factor Activity (TFA) from gene expression data is an important problem in systems biology. Currently, there exist various factor analysis methods for TRN reconstruction, but most approaches have specific assumptions not satisfied by real biological data. Network Component Analysis (NCA) can handle such limitations and is considered to be one of the most effective methods. The prerequisite for NCA is knowledge of the structure of TRN. Such structure can be obtained from ChIP-chip or ChIP-seq experiments, which however have quite limited applications. In order to cope with the difficulty, we resort to heuristic optimization algorithm such as Particle Swarm Optimization (PSO), in order to explore the possible structures of TRN and choose the most plausible one. Regarding the structure estimation problem, we extend classical PSO and propose a novel Probabilistic binary PSO. Furthermore, an improved NCA called FastNCA is adopted to compute the objective function accurately and fast, which enables PSO to run efficiently. Since heuristic optimization cannot guarantee global convergence, we run PSO multiple times and integrate the results. Then GCV-LASSO (Generalized Cross Validation - Least Absolute Shrinkage and Selection Operator) is performed to estimate TRN. We apply our approach and other factor analysis methods on the synthetic data. The results indicate that the proposed PSOFastNCA-GCV-LASSO algorithm gives better estimation. In order to incorporate more prior information on TRN structure and gene expression dynamics in the linear factor analysis model for improved estimation of TRN and TFAs, a linear Bayesian framework is adopted. Under the unified Bayesian framework, Bayesian Linear Sparse Factor Analysis Model (BLSFM) and Bayesian Linear State Space Model (BLSSM) are developed for instantaneous and dynamic TRN, respectively. Various approaches to incorporate partial and ambiguous prior network structure information in the Bayesian framework are proposed to improve performance in practical applications. Furthermore, we propose a novel mechanism for estimating the hyper-parameters of the distribution priors in our BLSFM and BLSSM models, which can significantly improve the estimation compared to traditional ways of hyper-parameter setting. With this development, reasonably good estimation of TFAs and TRN can be obtained even without use of any structure prior of TRN. Extensive numerical experiments are performed to investigate our developed methods under various settings, with comparison to some existing alternative approaches. It is demonstrated that our hyper-parameter estimation method improves the estimation of TFA and TRN in most settings and has superior performance, and that structure priors in general leads to improved estimation performance. Regarding application to real biological data, we execute the PSO-FastNCAGCV-LASSO algorithm developed in the thesis using E. Coli microarray data and obtain sensible estimation of TFAs and TRN. We apply BLSFM without structure priors of TRN, BLSSM without structure priors as well as with partial structure priors to Yeast S. cerevisiae microarray data and obtain a reasonable estimation of TFAs and TRN.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Henderson, David Allen. "Reconstruction of metabolic pathways by the exploration of gene expression data with factor analysis." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/30089.

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Microarray gene expression data for thousands of genes in many organisms is quickly becoming available. The information this data can provide the experimental biologist is powerful. This data may provide information clarifying the regulatory linkages between genes within a single metabolic pathway, or alternative pathway routes under different environmental conditions, or provide information leading to the identification of genes for selection in animal and plant genetic improvement programs or targets for drug therapy. Many analysis methods to unlock this information have been both proposed and utilized, but not evaluated under known conditions (e.g. simulations). Within this dissertation, an analysis method is proposed and evaluated for identifying independent and linked metabolic pathways and compared to a popular analysis method. Also, this same analysis method is investigated for its ability to identify regulatory linkages within a single metabolic pathway. Lastly, a variant of this same method is used to analyze time series microarray data. In Chapter 2, Factor Analysis is shown to identify and group genes according to membership within independent metabolic pathways for steady state microarray gene expression data. There were cases, however, where the allocation of all genes to a pathway was not complete. A competing analysis method, Hierarchical Clustering, was shown to perform poorly when negatively correlated genes are assumed unrelated, but performance improved when the sign of the correlation coefficient was ignored. In Chapter 3, Factor Analysis is shown to identify regulatory relationships between genes within a single metabolic pathway. These relationships can be explained using metabolic control analysis, along with external knowledge of the pathway structure and activation and inhibition of transcription regulation. In this chapter, it is also shown why factor analysis can group genes by metabolic pathway using metabolic control analysis. In Chapter 4, a Bayesian exploratory factor analysis is developed and used to analyze microarray gene expression data. This Bayesian model differs from a previous implementation in that it is purely exploratory and can be used with vague or uninformative priors. Additionally, 95% highest posterior density regions can be calculated for each factor loading to aid in interpretation of factor loadings. A correlated Bayesian exploratory factor analysis model is also developed in this chapter for application to time series microarray gene expression data. While this method is appropriate for the analysis of correlated observation vectors, it fails to group genes by metabolic pathway for simulated time series data.
Ph. D.
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Kröger, Stefan [Verfasser], Ulf [Gutachter] Leser, Joachim [Gutachter] Selbig, and Nils [Gutachter] Blüthgen. "Bioinformatic analyses for T helper cell subtypes discrimination and gene regulatory network reconstruction / Stefan Kröger ; Gutachter: Ulf Leser, Joachim Selbig, Nils Blüthgen." Berlin : Humboldt-Universität zu Berlin, 2017. http://d-nb.info/118933108X/34.

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Aravena, Duarte Andrés Octavio. "Probabilistic and constraint based modelling to determine regulation events from heterogeneous biological data." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00988255.

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This thesis proposes a method to build realistic causal regulatory networks hat has lower false positive rate than traditional methods. The first contribution of this thesis is to integrate heterogeneous information from two types of network predictions to determine a causal explanation of the observed gene co-expression. The second contribution is to model this integration as a combinatorial optimization problem. We demonstrate that this problem belongs to the NP-hard complexity class. The third contribution is the proposition of a heuristic approach to have an approximate solution in a practical execution time. Our evaluation shows that the E.coli regulatory network resulting from the application of this method has a higher accuracy than the putative one built with traditional tools. The bacterium Acidithiobacillus ferrooxidans is particularly challenging for the experimental determination of its regulatory network. Using the tools we developed, we propose a putative regulatory network and analyze it to rank the relevance of central regulators. In a second part of this thesis we explore how these regulatory relationships are manifested in a case linked to human health, developing a method to complete a linked to Alzheimer 's disease network. As an addendum we address the mathematical problem of microarray probe design. We conclude that, to fully predict the hybridization dynamics, we need a modified energy function for secondary structures of surface-attached DNA molecules and propose a scheme for determining such function.
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Molnar, Istvan, David Lopez, Jennifer Wisecaver, Timothy Devarenne, Taylor Weiss, Matteo Pellegrini, and Jeremiah Hackett. "Bio-crude transcriptomics: Gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa)*." BioMed Central, 2012. http://hdl.handle.net/10150/610020.

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BACKGROUND:Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy.RESULTS:A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated.CONCLUSIONS:The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome sequence for the Showa strain of B. braunii, race B. Further, the transcriptome database empowers future biosynthetic engineering approaches for strain improvement and the transfer of desirable traits to heterologous hosts.
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Books on the topic "Gene network reconstruction"

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Thomas, Greg. Border Blurs. Liverpool University Press, 2019. http://dx.doi.org/10.3828/liverpool/9781789620269.001.0001.

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This book presents the first in-depth account of the relationship between English and Scottish poets and the international concrete poetry movement of the 1950s-70s. Concrete poetry was a literary and artistic style which reactivated early-twentieth-century modernist impulses towards the merging of artistic media while simultaneously speaking to a gamut of contemporary contexts, from post-1945 social reconstruction to cybernetics, mass media, and the sixties counter-culture. The terms of its development in England and Scotland also suggest new ways of mapping ongoing complexities in the relationship between those two national cultures, and of tracing broader sociological and cultural trends in Britain during the 1960s-70s. Focusing especially on the work of Ian Hamilton Finlay, Edwin Morgan, Dom Sylvester Houédard, and Bob Cobbing, Border Blurs is based on new and extensive archival and primary research. It fills a gap in contemporary understandings of a significant literary and artistic genre which has been largely overlooked by literary critics. It also sheds new light on the development of British and Scottish literature during the late twentieth century, on the emergence of intermedia art, and on the development of modernism beyond its early-twentieth-century, urban Western networks.
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Meglin, Joellen A. Ruth Page. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780190205164.001.0001.

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In Ruth Page: The Woman in the Work, the Chicago ballerina emerges as a highly original choreographer who sought the iconoclastic as she transgressed boundaries of genre, gender, race, class, and sexuality in her art. Her works were often controversial—and sometimes censored—even as she succeeded in roles usually reserved for men in the ballet world: choreographer, artistic director, and impresario. From extensive dramaturgical analysis of her most famous ballets—La Guiablesse, Frankie and Johnny, Billy Sunday, Revenge, The Merry Widow, Camille, Carmina Burana, and Alice—to embodied reconstruction of an avant-garde solo performed in a “sack” designed by Isamu Noguchi, this book follows the global reach of Ruth Page’s career spanning the greater part of the twentieth century. A biography that disrupts notions that New York was the only cradle of the American ballet and George Balanchine its exponent to eclipse all others, Ruth Page explores the woman’s unique sensibility, corporeal praxis, and collaborative ethos to reveal her Chicago-centered network of creativity. In the process of discovering the woman in the work, we encounter an international cast of dancers (Anna Pavlova, Harald Kreutzberg, Frederic Franklin, Alicia Markova), composers (William Grant Still, Aaron Copland, Jerome Moross, Darius Milhaud), visual artists (Noguchi, Pavel Tchelitchew, Antoni Clavé), and companies (Ballet Russe de Monte Carlo, Ballets des Champs-Elysées, London Festival Ballet). But Page’s Chicago nucleus of dancers and artistic collaborators as well as Chicago institutions (Chicago Allied Arts, Federal Theatre Project, Lyric Opera of Chicago, Chicago Opera Ballet) crucially shaped her intermedial aesthetics.
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Book chapters on the topic "Gene network reconstruction"

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Allouche, David, Christine Cierco-Ayrolles, Simon de Givry, Gérald Guillermin, Brigitte Mangin, Thomas Schiex, Jimmy Vandel, and Matthieu Vignes. "A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context." In Gene Network Inference, 9–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45161-4_2.

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Sehgal, Muhammad Shoaib B., Iqbal Gondal, Laurence Dooley, Ross Coppel, and Goh Kiah Mok. "Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion." In Pattern Recognition in Bioinformatics, 274–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75286-8_27.

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Sehgal, Muhammad Shoaib, Iqbal Gondal, and Laurence Dooley. "Computational Modelling Strategies for Gene Regulatory Network Reconstruction." In Computational Intelligence in Medical Informatics, 207–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75767-2_10.

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Zheng, Ming, and Mugui Zhuo. "Gene Regulatory Network Reconstruction from Yeast Expression Time Series." In Lecture Notes in Electrical Engineering, 1477–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_191.

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Koumadorakis, Dimitrios E., Georgios N. Dimitrakopoulos, Marios G. Krokidis, and Aristidis G. Vrahatis. "Gene Regulatory Network Reconstruction Using Single-Cell RNA-Sequencing." In Handbook of Computational Neurodegeneration, 1–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-75479-6_18-1.

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Noman, Nasimul, Leon Palafox, and Hitoshi Iba. "Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model." In Proceedings in Information and Communications Technology, 93–103. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54394-7_8.

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Chang, Chunqi, Zhi Ding, and Yeung Sam Hung. "Nonnegative Network Component Analysis by Linear Programming for Gene Regulatory Network Reconstruction." In Independent Component Analysis and Signal Separation, 395–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00599-2_50.

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Dai, Jisheng, Chunqi Chang, Zhongfu Ye, and Yeung Sam Hung. "An Efficient Convex Nonnegative Network Component Analysis for Gene Regulatory Network Reconstruction." In Pattern Recognition in Bioinformatics, 56–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04031-3_6.

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Nair, Ajay, Madhu Chetty, and Pramod P. Wangikar. "Significance of Non-edge Priors in Gene Regulatory Network Reconstruction." In Neural Information Processing, 446–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_56.

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Remondini, Daniel, and Gastone Castellani. "Multiscale Network Reconstruction from Gene Expression Measurements: Correlations, Perturbations, and “A Priori Biological Knowledge”." In Applied Statistics for Network Biology, 105–31. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527638079.ch6.

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Conference papers on the topic "Gene network reconstruction"

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Mandal, Sudip, Goutam Saha, and Rajat Kumar Pal. "Neural network based gene regulatory network reconstruction." In 2015 3rd International Conference on Computer, Communication, Control and Information Technology (C3IT). IEEE, 2015. http://dx.doi.org/10.1109/c3it.2015.7060112.

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"Neurotransmitter gene network reconstruction and analisis." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-177.

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Bezerra, G. B., T. V. Barra, F. J. von Zuben, and L. N. de Castro. "Handling Data Sparseness in Gene Network Reconstruction." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594900.

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Sehgal, Muhammad Shoaib B., Iqbal Gondal, Laurence Dooley, and Ross Coppel. "Coalesce Gene Regulatory Network Reconstruction: A Cross-Platform Transcriptional Gene Network Fusion Framework." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.343719.

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Qu, Luxuan, Zhiqiong Wang, Yueyang Huo, Yuezhou Zhou, Junchang Xin, and Wei Qian. "Distributed Local Bayesian Network for Gene Regulatory Network Reconstruction." In 2020 6th International Conference on Big Data Computing and Communications (BIGCOM). IEEE, 2020. http://dx.doi.org/10.1109/bigcom51056.2020.00026.

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Yang, Bo, Junying Zhang, Junliang Shang, and Aimin Li. "A Bayesian network based algorithm for gene regulatory network reconstruction." In 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2011. http://dx.doi.org/10.1109/icspcc.2011.6061811.

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SEMAN, ALI, MOHAMED SAIFULAMAN, SHARIFALLILAH NORDIN, and WINDDY PINDAH. "Reconstruction of Gene Regulatory Network from Gene Perturbation Data Current Methods and Problems." In Fifth International Conference On Advances in Computing, Electronics and Electrical Technology - CEET 2016. Institute of Research Engineers and Doctors, 2016. http://dx.doi.org/10.15224/978-1-63248-087-3-51.

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Kovalev, Sergey S., Arthur I. Dergilev, Yuriy L. Orlov, Oleg D. Fateev, and Urana N. Kavai-ool. "Reconstruction of Dementia Gene Network Using Online Bioinformatics Tools." In 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB). IEEE, 2020. http://dx.doi.org/10.1109/csgb51356.2020.9214618.

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"Gene network of type 2 diabetes: reconstruction and analysis." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-125.

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Quarton, Tyler, Taek Kang, Eduardo D. Sontag, and Leonidas Bleris. "Exploring the impact of resource limitations on gene network reconstruction." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798773.

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