title: Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation.
abstract: 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.
authors.affiliation: Computer Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom. Electronic address: np394@cam.ac.uk.
authors.forename: Naruemon
authors.initials: N
authors.lastname: Pratanwanich
authors.affiliation: Computer Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom. Electronic address: pl219@cam.ac.uk.
authors.forename: Pietro
authors.initials: P
authors.lastname: Lio
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002839:Chromans / Q000494:pharmacology; D003907:Dexamethasone / Q000494:pharmacology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000187:drug effects*; D053263:Gene Regulatory Networks / Q000187:drug effects*; D019833:Genistein / Q000494:pharmacology; D015894:Genome, Human / Q000187:drug effects*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000187:drug effects* / Q000235:genetics; D015233:Models, Statistical; D010597:Pharmacogenetics; D011441:Propylthiouracil / Q000494:pharmacology; D045162:Thiazolidinediones / Q000494:pharmacology; D000077288:Troglitazone
chemical_list: D002839:Chromans; D045162:Thiazolidinediones; D011441:Propylthiouracil; D003907:Dexamethasone; D019833:Genistein; D000077288:Troglitazone
keywords: Bayesian inference; Biological pathways; Functional genomics; Genome complexity; Latent Dirichlet allocation (LDA)