期刊
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
卷 14, 期 1, 页码 119-128出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2009.2033116
关键词
Classification; gene pathway; kernel-based method; system biology
类别
资金
- National Institutes of Health/National Library of Medicine [1 R33 LM07299-01]
- NATIONAL LIBRARY OF MEDICINE [R33LM007299] Funding Source: NIH RePORTER
Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.
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