Bayesian network learning with feature abstraction for gene-drug dependency analysis.
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.
Chang
Jeong-Ho
JH
Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jhchang@bi.snu.ac.kr
Hwang
Kyu-Baek
KB
Oh
S June
SJ
Zhang
Byoung-Tak
BT
D000465:Algorithms; D000970:Antineoplastic Agents / Q000008:administration & dosage*; D001185:Artificial Intelligence; D016208:Databases, Factual; D015195:Drug Design; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects*; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015398:Signal Transduction / Q000187:drug effects
D000970:Antineoplastic Agents; D009363:Neoplasm Proteins