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Bibliographies thématiques / Gene network reconstruction

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Littérature scientifique sur le sujet « Gene network reconstruction »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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Thèses sur le sujet "Gene network reconstruction"

1

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 network

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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<br>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.<br>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

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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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4

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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5

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

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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

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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 propose

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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 pract

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Molnar, Istvan, David Lopez, Jennifer Wisecaver, et al. "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 a

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