Bayesian network learning with feature abstraction for gene-drug dependency analysis.

Jeong-Ho Chang; Kyu-Baek Hwang; S June Oh; Byoung-Tak Zhang

doi:10.1142/s0219720005000874

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Bayesian network learning with feature abstraction for gene-drug dependency analysis.

Jeong-Ho Chang·Kyu-Baek Hwang·S June Oh·Byoung-Tak Zhang

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Abstract

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

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Algorithms Antineoplastic Agents Artificial Intelligence Databases, Factual Drug Design Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Neoplasm Proteins Neoplasms Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Signal Transduction Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Jeong-Ho Chang, Kyu-Baek Hwang, S June Oh, Byoung-Tak Zhang. 2005. Bayesian network learning with feature abstraction for gene-drug dependency analysis.. https://doi.org/10.1142/s0219720005000874

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