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The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6023))

Abstract

A primary goal of human genetics is the discovery of genetic factors that influence individual susceptibility to common human diseases. This problem is difficult because common diseases are likely the result of joint failure of two or more interacting components instead of single component failures. Efficient algorithms that can detect interacting attributes are needed. The Relief family of machine learning algorithms, which use nearest neighbors to weight attributes, are a promising approach. Recently an improved Relief algorithm called Spatially Uniform ReliefF (SURF) has been developed that significantly increases the ability of these algorithms to detect interacting attributes. Here we introduce an algorithm called SURF* which uses distant instances along with the usual nearby ones to weight attributes. The weighting depends on whether the instances are are nearby or distant. We show this new algorithm significantly outperforms both ReliefF and SURF for genetic analysis in the presence of attribute interactions. We make SURF* freely available in the open source MDR software package. MDR is a cross-platform Java application which features a user friendly graphical interface.

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Authors and Affiliations

  1. Dartmouth Medical School, Lebanon, NH, 03756, USA

    Casey S. Greene, Daniel S. Himmelstein, Jeff Kiralis & Jason H. Moore

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  1. Casey S. Greene
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  2. Daniel S. Himmelstein
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  3. Jeff Kiralis
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  4. Jason H. Moore
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Editor information

Editors and Affiliations

  1. Institute for High-Performance Computing and Networking (ICAR), Italian National Research Council (CNR), Via P. Bucci 41C, 87036, Rende, (CS), Italy

    Clara Pizzuti

  2. Department of Molecular Physiology and Biophysics, Vanderbilt University, Center for Human Genetics Research, 519 Light Hall, 37232, Nashville, TN, USA

    Marylyn D. Ritchie

  3. Department of Animal Production Epidemiology and Ecology, University of Torino, Molecular Biotechnology Center, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

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Greene, C.S., Himmelstein, D.S., Kiralis, J., Moore, J.H. (2010). The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-12211-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12210-1

  • Online ISBN: 978-3-642-12211-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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