A fast approach to detect gene-gene synergy

文献类型： 外文期刊

第一作者： Chen, Yuan

作者： Chen, Yuan;Yuan, Zheming;Xing, Pengwei;Chen, Yuan;Yuan, Zheming;Gao, Jun;Bai, Lianyang

作者机构：

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.379； 五年影响因子：5.133 ）

ISSN： 2045-2322

年卷期： 2017 年 7 卷

页码：

收录情况： SCI

摘要： Selecting informative genes, including individually discriminant genes and synergic genes, from expression data has been useful for medical diagnosis and prognosis. Detecting synergic genes is more difficult than selecting individually discriminant genes. Several efforts have recently been made to detect gene-gene synergies, such as dendrogram-based I(X-1; X-2; Y) (mutual information), doublets (gene pairs) and MIC(X-1; X-2; Y) based on the maximal information coefficient. It is unclear whether dendrogram-based I(X-1; X-2; Y) and doublets can capture synergies efficiently. Although MIC(X-1; X-2; Y) can capture a wide range of interaction, it has a high computational cost triggered by its 3-D search. In this paper, we developed a simple and fast approach based on abs conversion type (i.e. Z = vertical bar X-1-X-2 vertical bar) and t-test, to detect interactions in simulation and real-world datasets. Our results showed that dendrogram-based I(X-1; X-2; Y) and doublets are helpless for discovering pair-wise gene interactions, our approach can discover typical pair-wise synergic genes efficiently. These synergic genes can reach comparable accuracy to the individually discriminant genes using the same number of genes. Classifier cannot learn well if synergic genes have not been converted properly. Combining individually discriminant and synergic genes can improve the prediction performance.

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