Gene Interaction-Level Cancer Classification using Gene Expression Profiles

Abstract

Recent studies suggest that biological pathways have the power to be stronger biomarkers for cancer than individual genes. The knowledgebase of pathways contains the interactions among the genes. However, it is not necessary for all the genes in a pathway to interact with each other. Closely interacting genes are supposed to have a collective effect to cause cancer or other disease. Here we propose a novel cancer classification method utilizing the collective effect of the set of closely interacting genes which we call Gene Interaction Set (GIS). We first find out the possible strength levels of each gene interaction set using clustering method and then rank all the sets with our proposed entropy metric using the proportion of samples of different classes having same strength level and finally predict the class of a new sample by weighted voting of top k gene interaction sets. The important feature of our method is that the process of causing the disease can easily be figured out. We validate our method comparing with other classification methods known to produce very high accuracy on 7 cancer datasets.

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