title: GroupRank: rank candidate genes in PPI network by differentially expressed gene groups.
abstract: Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-expressed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Siyi
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Shichao
authors.initials: S
authors.lastname: Pang
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Menghuan
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Wang
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Shanghai Center for Bioinformation Technology, Shanghai, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D007680:Kidney Neoplasms / Q000235:genetics / Q000473:pathology; D007938:Leukemia / Q000235:genetics / Q000473:pathology; D008962:Models, Theoretical; D060066:Protein Interaction Maps / Q000235:genetics*