Tuning ReliefF for Genome-Wide Genetic Analysis

  • Jason H. Moore
  • Bill C. White
Conference paper

DOI: 10.1007/978-3-540-71783-6_16

Volume 4447 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Moore J.H., White B.C. (2007) Tuning ReliefF for Genome-Wide Genetic Analysis. In: Marchiori E., Moore J.H., Rajapakse J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg

Abstract

An important goal of human genetics is the identification of DNA sequence variations that are predictive of who is at risk for various common diseases. The focus of the present study is on the challenge of detecting and characterizing nonlinear attribute interactions or dependencies in the context of a genome-wide genetic study. The first question we address is whether the ReliefF algorithm is suitable for attribute selection in this domain. The second question we address is whether we can improve ReliefF for selecting important genetic attributes. Using simulated genetic datasets, we show that ReliefF is significantly better than a naïve chi-square test of independence for selecting two interacting attributes out of 103 candidates. In addition, we show that ReliefF can be improved in this domain by systematically removing the worst attributes and re-estimating ReliefF weights. Our simulation studies demonstrate that this new Tuned ReliefF (TuRF) algorithm is significantly better than ReliefF.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jason H. Moore
    • 1
  • Bill C. White
    • 1
  1. 1.Dartmouth College, One Medical Center Drive, NH 03756Lebanon