Jump to ContentJump to Main Navigation
Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes$
Users without a subscription are not able to see the full content.

Michael Windle

Print publication date: 2016

Print ISBN-13: 9780262034685

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262034685.001.0001

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use (for details see http://www.mitpress.universitypressscholarship.com/page/privacy-policy).date: 18 August 2018

An Overview of the RELIEF Algorithm and Advancements

An Overview of the RELIEF Algorithm and Advancements

Chapter:
(p.95) 6 An Overview of the RELIEF Algorithm and Advancements
Source:
Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes
Author(s):

Alexandre Todorov

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262034685.003.0006

The aim of the RELIEF algorithm is to filter out features (e.g., genes, environmental factors) that are relevant to a trait of interest, starting from a set of that may include thousands of irrelevant features. Though widely used in many fields, its application to the study of gene-environment interaction studies has been limited thus far. We provide here an overview of this machine learning algorithm and some of its variants. Using simulated data, we then compare of the performance of RELIEF to that of logistic regression for screening for gene-environment interactions in SNP data. Even though performance degrades in larger sets of markers, RELIEF remains a competitive alternative to logistic regression, and shows clear promise as a tool for the study of gene-environment interactions. Areas for further improvements of the algorithm are then suggested.

Keywords:   Gene-Environment Interaction, Relief Algorithm, Machine Learning

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.