title: Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction.
abstract: Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonlinear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.
authors.affiliation: Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, SC 29208, United States.
authors.forename: Zhonghao
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, SC 29208, United States. Electronic address: jianjunh@cse.sc.edu.
authors.forename: Jianjun
authors.initials: J
authors.lastname: Hu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D005544:Forecasting; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics
keywords: Cancer gene; Disease gene identification; Gene expression; Protein localization