TY - JOUR
T1 - Gene function prediction with gene interaction networks
T2 - A context graph kernel approach
AU - Li, Xin
AU - Chen, Hsinchun
AU - Li, Jiexun
AU - Zhang, Zhu
N1 - Funding Information:
Manuscript received November 6, 2008; revised March 26, 2009 and August 14, 2009. First published September 29, 2009; current version published January 15, 2010. This work was supported by the National Institutes of Health/National Library of Medicine under Grant 1 R33 LM07299-01. X. Li is with the Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong (e-mail: [email protected]). H. Chen and Z. Zhang are with the Department of Management Information Systems, University of Arizona, Tucson, AZ 85721 USA (e-mail: [email protected]; [email protected]). J. Li is with the College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITB.2009.2033116
PY - 2010/1
Y1 - 2010/1
N2 - 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.
AB - 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.
KW - Classification
KW - Gene pathway
KW - Kernel-based method
KW - System biology
UR - http://www.scopus.com/inward/record.url?scp=76849095353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=76849095353&partnerID=8YFLogxK
U2 - 10.1109/TITB.2009.2033116
DO - 10.1109/TITB.2009.2033116
M3 - Article
C2 - 19789115
AN - SCOPUS:76849095353
SN - 1089-7771
VL - 14
SP - 119
EP - 128
JO - IEEE Transactions on Information Technology in Biomedicine
JF - IEEE Transactions on Information Technology in Biomedicine
IS - 1
M1 - 5272443
ER -