Journal
COMPUTATIONAL BIOLOGY AND CHEMISTRY
Volume 53, Issue -, Pages 144-152Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2014.08.019
Keywords
Biological pathways; Functional genomics; Genome complexity; Latent Dirichlet allocation (LDA); Bayesian inference
Funding
- Royal Thai Government scholarship
- Progetto Lagrange-Fondazione CRT
- Department of Health Sciences Novara - Italy
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Analysis of cellular responses to diverse stimuli enables the exploration in the complexity of functional genomics. Typically, high-throughput microarray data allow us to identify genes that are differentially expressed under a phenomenon of interest. To extract the meanings from the long list of those differentially expressed genes, we present a new method pathway-based LDA to determine pathways/gene sets that are perturbed after exposure to different chemicals. In this study, a pathway is defined as a group of functionally related genes. Specifically, we have implemented a probabilistic Latent Dirichlet Allocation (LDA) model to learn drug-pathway-gene relations by taking known gene-pathway memberships as prior knowledge. We applied the pathway-based LDA model and 236 known pathways in order to determine pathway responsiveness to gene expression data of 1169 drugs. Our method yielded a better predictive performance on pathway responsiveness to drug treatments than the existing methods. Moreover, the pathway-based LDA also revealed genes contributing the most in each pre-defined pathway through a probabilistic distribution of genes. In achieving that, our method could provide a useful estimator of the pathway complexity of a genome. (C) 2014 Elsevier Ltd. All rights reserved.
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