Abstract
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.
Original language | English (US) |
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Article number | 5272443 |
Pages (from-to) | 119-128 |
Number of pages | 10 |
Journal | IEEE Transactions on Information Technology in Biomedicine |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2010 |
Keywords
- Classification
- Gene pathway
- Kernel-based method
- System biology
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering