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Responsible for many complex human diseases including cancers, disrupted or abnormal gene interactions can be identified through their expression changes correlating with the progression of a disease. However, the examination of all possible combinatorial interactions between gene features in a genome-wide case-control study is computationally infeasible as the search space is exponential in nature. In this paper, we propose a novel computational approach, QUIRE, to identify discriminative complex interactions among informative gene features for cancer diagnosis. QUIRE works in two stages, where it first identifies functionally relevant feature groups for the disease and, then explores the search space capturing the combinatorial relationships among the genes from the selected informative groups. Using QUIRE, we explore the differential patterns and the interactions among informative gene features in three different types of cancers, Renal Cell Carcinoma(RCC), Ovarian Cancer(OVC) and Colorectal Cancer (CRC). Our experimental results show that QUIRE identifies gene-gene interactions that can better identify the different cancer stages of samples and can predict CRC recurrence and death from CRC more successfully, as compared to other state-of-the-art feature selection methods. A literature survey shows that many of the interactions identified by QUIRE play important roles in the development of cancer. |
