Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases

Authors:
Delaney Granizo-Mackenzie;Jason H. Moore
Affiliations:
Dartmouth College, Hanover, NH;Dartmouth College, Hanover, NH
Venue:
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2013

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Abstract

Detecting genetic interactions without running an exhaustive search is a difficult problem. We present a new heuristic, multiSURF*, which can detect these interactions with high accuracy and in time linear in the number of genes. Our algorithm is an improvement over the SURF* algorithm, which detects genetic signals by comparing individuals close to, and far from, one another and noticing whether differences correlate with different disease statuses. Our improvement consistently outperforms SURF* while providing a large runtime decrease by examining only individuals very near and very far from one another. Additionally we perform an analysis on real data and show that our method provides new information. We conclude that multiSURF* is a better alternative to SURF* in both power and runtime.