Cancer Diagnosis with QUIRE: QUadratic Interactions among infoRmative fEatures
Salim Chowdhury, Yanjun Qi, Alex Stewart, Rachel Ostroff, Renqiang Min

TL;DR
This paper introduces QUIRE, a novel computational method that identifies complex gene interactions for cancer diagnosis, effectively distinguishing cancer stages and predicting outcomes by focusing on informative gene groups.
Contribution
QUIRE is a new two-stage approach that efficiently captures combinatorial gene interactions relevant to cancer, outperforming existing feature selection methods.
Findings
Identifies gene interactions that improve cancer stage classification.
Predicts colorectal cancer recurrence and mortality more accurately.
Reveals biologically significant gene interactions involved in cancer development.
Abstract
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…
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Taxonomy
TopicsGene expression and cancer classification · Bioinformatics and Genomic Networks · Machine Learning in Bioinformatics
