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

•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 locat...

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Bibliographic Details
Published inMethods (San Diego, Calif.) Vol. 93; pp. 119 - 127
Main Authors Liu, Zhonghao, Hu, Jianjun
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.01.2016
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Summary:•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.
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ISSN:1046-2023
1095-9130
DOI:10.1016/j.ymeth.2015.09.022