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- W2058993911 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." @default.
- W2058993911 created "2016-06-24" @default.
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- W2058993911 date "2014-12-01" @default.
- W2058993911 modified "2023-09-26" @default.
- W2058993911 title "Exploring the complexity of pathway–drug relationships using latent Dirichlet allocation" @default.
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- W2058993911 doi "https://doi.org/10.1016/j.compbiolchem.2014.08.019" @default.
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