Knowledge-guided gene ranking by coordinative component analysis |
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| Authors: | Chen Wang Jianhua Xuan Huai Li Yue Wang Ming Zhan Eric P Hoffman Robert Clarke |
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| Institution: | (1) Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA;(2) Bioinformatics Unit, Research Resources Branch, National Institute on Aging, NIH, Baltimore, MD, USA;(3) Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, USA;(4) Departments of Oncology and Physiology & Biophysics, Georgetown University School of Medicine, Washington, DC, USA |
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| Abstract: | Background In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus,
it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical
methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge
into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true
interplay between biological knowledge and gene expression data. |
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