Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"inferred networks"”.
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Inference of Gene Regulatory Networks with integration of prior knowledge
Gene regulatory networks (GRNs) are crucial for understanding complex biological processes and disease mechanisms, particularly in cancer. However, GRN inference remains challenging due to the intricate nature of gene interactions and limitations of existing methods. Traditionally, prior knowledge …
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Dissecting Transcriptional Regulatory Networks with Systems Biology Approaches
… biology approaches. Transcription regulatory networks play an important role in mediating external stimuli and coordinating responses to changing environments. Different methods that infer regulatory interactions directly from microarray data have been developed in the recent past. However, …
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Parasitism, Predation Prediction: Modelling Variations in Ecological Interactions from Mechanisms to Networks
… as in the topology of ecological interaction networks. The main objectives of my PhD thesis were to improve the frameworks and methodologies used for predicting and quantifying variations in interactions and their processes and to understand how intraspecific variation can influence the …
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UNDERSTANDING DIFFERENTIAL ABUNDANCE IN MICROBIAL ECOLOGY USING COMMUNITY STRUCTURE.
… classification over the complex interaction networks. This thesis introduces a novel framework that reframes DAA through probabilistic graphical model inference, integrating network analysis with abundance profiling to uncover not only key microbial features but also the intricate …
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Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii
… We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria …