Relevant bibliographies by topics / Gene network reconstruction / Dissertations / Theses
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Dissertations / Theses on the topic 'Gene network reconstruction'

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Published: 9 March 2023
Last updated: 10 March 2023

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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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2

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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3

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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6

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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7

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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8

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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9

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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10

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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11

Werhli, Adriano Velasque. "Reconstruction of gene regulatory networks from postgenomic data." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/3198.

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An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. The recent substantial increase in the availability of such data has stimulated the interest in inferring the networks and pathways from the data themselves. The main interests of this thesis are the application, evaluation and the improvement of machine learning methods applied to the reverse engineering of biochemical pathways and networks. The thesis starts with the application of an established method to newly available gene expression data related to the interfer
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12

Deng, Wenping. "Algorithms for Reconstruction of Gene Regulatory Networks from High-Throughput Gene Expression Data." Thesis, Michigan Technological University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420080.

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<p> Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with i
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13

Kentzoglanakis, Kyriakos. "Reconstructing gene regulatory networks : a swarm intelligence framework." Thesis, University of Portsmouth, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523619.

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14

Groß, Torsten. "Network Inference from Perturbation Data: Robustness, Identifiability and Experimental Design." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22355.

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Hochdurchsatzverfahren quantifizieren eine Vielzahl zellulärer Komponenten, können aber selten deren Interaktionen beschreiben. Daher wurden in den letzten 20 Jahren verschiedenste Netzwerk-Rekonstruktionsmethoden entwickelt. Insbesondere Perturbationsdaten erlauben dabei Rückschlüsse über funktionelle Mechanismen in der Genregulierung, Signal Transduktion, intra-zellulärer Kommunikation und anderen Prozessen zu ziehen. Dennoch bleibt Netzwerkinferenz ein ungelöstes Problem, weil die meisten Methoden auf ungeeigneten Annahmen basieren und die Identifizierbarkeit von Netzwerkkanten nicht aufklä
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15

Pournara, Iosifina-Vasiliki. "Reconstructing gene networks by passive and active Bayesian learning." Thesis, Birkbeck (University of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417001.

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16

Thomas, Spencer Angus. "Synthesis, analysis and reconstruction of gene regulatory networks using evolutionary algorithms." Thesis, University of Surrey, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659110.

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Large and complex biological networks are thought to be built from small functional modules called motifs. Currently there has been insufficient study of the fundamental understanding of these motifs which has resulted in a lack of consensus of their role and presence in biology. Here we investigate two networks that produce biologically important dynamics, an oscillation and a toggle switch. We couple these motifs and observe multiple sets of combined dynamic behaviour and evidence of 'gene connectivity preferences between the two networks. Such fundamental studies of networks can be performe
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17

Malysheva, Valeriya. "Reconstruction of gene regulatory networks defining the cell fate transition processes." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAJ084/document.

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L’établissement de l’identité cellulaire est un phénomène très complexe qui implique pléthore de signaux instructifs intrinsèques et extrinsèques. Cependant, malgré les progrès importants qui ont été faits pour l’identification des régulateurs clés, les liens mécanistiques entre facteurs de transcription, épigénome, et structure de la chromatine lors de la différenciation cellulaire, et de la transformation tumorigénique des cellules, sont peu connus. Pour résoudre ces problématiques nous avons utilisé deux modèles de transition de l’identité cellulaire : la différenciation neuronale et endode
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18

Ghanbari, Mahsa [Verfasser]. "Association measures and prior information in the reconstruction of gene networks / Mahsa Ghanbari." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1104733757/34.

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19

Liu, Jinhua [Verfasser]. "Bioinformatic Reconstruction of Gene Regulatory Networks Controlling EMT and Mesoderm Formation / Jinhua Liu." Berlin : Freie Universität Berlin, 2020. http://d-nb.info/1218530537/34.

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20

Hasegawa, Takanori. "Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195985.

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21

Ait-Hamlat, Adel. "Reconstruction de réseaux de gènes à partir de données d'expression par déconvolution centrée autour des hubs." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS011.

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Les réseaux de régulation de gènes (RRG) sont des graphes dans lesquels les nœuds sont des gènes et les arcs représentent les relations de régulation entre gènes régulateurs et gènes cibles. La topologie d’un RRG est caractérisée par un petit nombre de gènes qui ont un grand nombre de connexions alors que la majorité des autres gènes a peu de connexions. Les nœuds hautement connectés s’appellent des hubs ; ils permettent à deux nœuds quelconques d’être connectés par des chemins relativement courts dans les réseaux peu denses que sont les RRGs. HubNeD (Hub-centered network deconvolution) est un
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22

江益志. "Integrating gene expression, SNP markers, and gene-gene interaction towards network reconstruction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/97695667099035745199.

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23

Lai, Jhih-Siang, and 賴至祥. "Graph-Based Clustering Approaches for Gene Network Reconstruction." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/17481425002494051273.

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碩士<br>國立臺灣大學<br>醫學工程學研究所<br>97<br>To understand regulatory relationships between genes in real life. Biologists often use RNA interference (RNAi) or knockout genes to observe the response in the real life system. Informationists try to reconstruct regulatory relationship between genes from mRNA expression profile by algorithms or mathematic models. There are several phases involved in gene regulation such as transcription, post-transcriptional modifications, translation, mRNA degradation and post-translational modifications .Time is essential for all these phases to be completed and many resea
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24

"Computational models for efficient reconstruction of gene regulatory network." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075380.

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Zhang, Qing.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (leaves 129-148).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstract also in Chinese.
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25

Lin, Chung-Hsun, and 林仲訓. "Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/83763564848362542147.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>101<br>In recent years, using microarray time series data to reconstruct gene regulatory network, has become a very popular way. However, the number of these genes are usually very large. We want to rebuild before these genes do a proper clustering, which is the each other interaction between the genes will be divided in the same group. The way we use here is to combine multiple data sources. On the one hand avoid being affected by the impact of a single data source. When there is only one data source, data quality will have a great impact. On the other hand, we ho
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26

Richard, Guilhem. "Affecting the macrophage response to infection by integrating signaling and gene-regulatory networks." Thesis, 2014. https://hdl.handle.net/2144/14270.

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Obesity has reached epidemic proportions in recent years. The World Health Organization estimated in 2008 that 1.4 billion people were overweight of whom 500 million were obese. Obesity associates with a wide range of conditions, such as cardiovascular diseases, cancer, diabetes, and neurological disorders, and causes approximately 2.8 million deaths each year. Many studies have established that obesity strongly impacts the normal function of the immune system: it dysregulates production of inflammatory and anti&ndash;inflammatory cytokines, alters numbers of immune cells, and causes an overal
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27

Padolina, Joanna Melinda. "Phylogenetic reconstruction of Phalaenopsis (Orchidaceae) using nuclear and chloroplast DNA sequence data and using Phalaenopsis as a natural system for assessing methods to reconstruct hybrid evolution in phylogenetic analyses." 2006. http://hdl.handle.net/2152/20172.

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Two phylogenies of Phalaenopsis (Orchidaceae) are presented, one from combined chloroplast DNA data and one from a nuclear actin gene. We used these phylogenies to assess and modify the classification of Phalaenopsis and to examine several morphological characters and geographical distribution patterns. Our results support Christenson’s (2001) treatment of Phalaenopsis as a broadly defined genus that includes the species previously placed in the genera Doritis and Kingidium. Some of Christenson’s subgeneric groups needed to be recircumscribed to reflect a natural classification. We recognized
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28

Chang, Chih-Jung, and 張志榮. "Gene Networks Reconstruction based on Structural Equation Modeling." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/21699798502766537048.

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碩士<br>臺灣大學<br>醫學工程學研究所<br>96<br>With the continual progress of human genome researches, more and more genes have been found to be closely related to human diseases. Accordingly, exploration of genetic functions has become one of major foci in biotechnology researches. It is well known that each gene does not work alone. Instead, it may involve enormous complicated interactions among genes in a biological process. Because of the complexity of physiological and biochemical processes in biology, the relations between the genes and most diseases are not clear currently. Therefore, the ultimate goa
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29

"Reconstructing gene regulatory networks with new datasets." 2013. http://library.cuhk.edu.hk/record=b5549309.

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競爭性內源核糖核酸(ceRNA) 假設最近已成為生物訊息學研究中最熱門的話題之一。Cell 是在生物科學界上經常被引用的學術期刊，早前亦有一班學者在Cell 2011年同一期成功發佈四篇關於ceRNA 假設的學術文章。跟據有關ceRNA 假設的學術文章，大部份學者均以不同的個別例子成功驗證假定，可是，欠缺一個大規模的及全面性的分析。<br>在我兩年碩士的研究中，我引入了一個新的概念微核糖核酸及其目標對向聚類(MTB) 運用了ceRNA 的假設，還提出算法，成功從微核糖核酸與信使核糖核酸的相互數據中找出一系列的MTB' 還利用GENCODE 項目上大量的微核糖核酸及信使核糖核酸的表達數據去驗証MTB 的概念。一方面，我從大量的表達數據中成功推斷出微核糖核酸與信使核糖核酸之間的相反關連、信使核糖核酸之間的正面關運和微核糖核酸之間的正面關連;另一方面，這些關連進一步肯定ceRNA 假設的真實性。此外，我提出一個從大量基因組中找出基因功能分析的方法，並在大量的MTB 的基因組中找出重要的基因註解。最後，我提出另一個MTB 概念的應用一新算法來預測微核糖核酸與信使核糖核酸的相互影響。總括而吉， MTB 概念從複雜且混亂的微核糖核酸與信使核糖核酸網絡中定義簡單且穩固的模姐，提供一個系統生物學分析微核糖核酸調節能力的方法。<br>The competing Endogenous RNA (ceR
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30

Whitehead, Dion [Verfasser]. "Reconstructing gene function and gene regulatory networks in prokaryotes / by Dion Whitehead." 2005. http://d-nb.info/977558460/34.

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31

Su, Chu-Hsien, and 蘇矩賢. "Reconstruction of Interaction Networks of Escherichia coli through Literature Mining of Gene-Gene Relations." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/80389978697198390403.

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碩士<br>國立陽明大學<br>生物醫學資訊研究所<br>102<br>Previous studies of reconstructing interaction networks indicated that network-based methods represent the relationships between genes and gene products of the target organisms. In this study we used E. coli K-12 MG1655, an important model organism, as the target organism for reconstructing interaction networks. Interactions of E. coli large molecules are usually obtained from databases and literature. Most interactions in the databases are lacking of literature supports. It is challenging to retrieve traceable literature citations for these interactions of
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