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 = |X 1 - X 2|) 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.
