Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering
نویسندگان
چکیده
منابع مشابه
Gene co-expression networks via biclustering Differential gene co-expression networks via Bayesian biclustering models
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
متن کاملContext Specific and Differential Gene Co-expression Networks via Bayesian Biclustering
Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-...
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Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
متن کاملBayesian Biclustering of Gene Expression
Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results: We developed a Bayesia...
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Inferring genetic networks is of great importance in unlocking gene behaviour, which in turn provides solutions for drug testing, disease resistance, and many other applications. Dynamic network models provide room for handling noisy or missing prelearned data. This paper discusses how Dynamic Bayesian Networks compare against coexpression networks as discussed by Zhang and Horvath [1]. These s...
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ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2016
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1004791