Literature DB >> 25403540

Epistasis analysis using ReliefF.

Jason H Moore1.   

Abstract

Here we introduce the ReliefF machine learning algorithm and some of its extensions for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few examples of published studies of complex human diseases that have used ReliefF.

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Year:  2015        PMID: 25403540     DOI: 10.1007/978-1-4939-2155-3_17

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  9 in total

1.  Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS.

Authors:  Elisabetta Manduchi; Scott M Williams; Alessandra Chesi; Matthew E Johnson; Andrew D Wells; Struan F A Grant; Jason H Moore
Journal:  Hum Genet       Date:  2018-05-24       Impact factor: 4.132

Review 2.  Brief Survey on Machine Learning in Epistasis.

Authors:  Davide Chicco; Trent Faultless
Journal:  Methods Mol Biol       Date:  2021

Review 3.  Relief-based feature selection: Introduction and review.

Authors:  Ryan J Urbanowicz; Melissa Meeker; William La Cava; Randal S Olson; Jason H Moore
Journal:  J Biomed Inform       Date:  2018-07-18       Impact factor: 6.317

4.  ExSTraCS 2.0: Description and Evaluation of a Scalable Learning Classifier System.

Authors:  Ryan J Urbanowicz; Jason H Moore
Journal:  Evol Intell       Date:  2015-04-03

Review 5.  Genomics models in radiotherapy: From mechanistic to machine learning.

Authors:  John Kang; James T Coates; Robert L Strawderman; Barry S Rosenstein; Sarah L Kerns
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 6.  Machine Learning and Radiogenomics: Lessons Learned and Future Directions.

Authors:  John Kang; Tiziana Rancati; Sangkyu Lee; Jung Hun Oh; Sarah L Kerns; Jacob G Scott; Russell Schwartz; Seyoung Kim; Barry S Rosenstein
Journal:  Front Oncol       Date:  2018-06-21       Impact factor: 6.244

Review 7.  How to increase our belief in discovered statistical interactions via large-scale association studies?

Authors:  K Van Steen; J H Moore
Journal:  Hum Genet       Date:  2019-03-06       Impact factor: 4.132

8.  Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.

Authors:  Alexa A Woodward; Deanne M Taylor; Elizabeth Goldmuntz; Laura E Mitchell; A J Agopian; Jason H Moore; Ryan J Urbanowicz
Journal:  BioData Min       Date:  2022-02-12       Impact factor: 4.079

9.  Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS.

Authors:  Elisabetta Manduchi; Alessandra Chesi; Molly A Hall; Struan F A Grant; Jason H Moore
Journal:  Pac Symp Biocomput       Date:  2018
  9 in total

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