Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

• Published in: Methods (San Diego, Calif.) Vol. 93; pp. 119 - 127
• Main Authors: Liu, Zhonghao, Hu, Jianjun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2016
• Subjects: Cancer gene; Computational Biology - methods; Disease gene identification; Forecasting; Gene expression; Gene Expression Regulation, Neoplastic - genetics; Gene Regulatory Networks - genetics; Genetic Association Studies - methods; Humans; Neoplasms - genetics; Protein localization; Protein localization; Disease gene identification; Gene expression; Cancer gene
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2015.09.022

•Predicting cancer disease genes related to protein mislocalization.•Complementary to genome sequencing for identifying cancer genes.•Generating hypothesis on potential mechanisms underlying cancer. 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.