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Grammatical evolution support vector machines for predicting human genetic disease association

Authors:

Skylar Marvel,

Alison Motsinger-ReifAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 595 - 598

https://doi.org/10.1145/2330784.2330881

Published: 07 July 2012 Publication History

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Abstract

Identifying genes that predict common, complex human diseases is a major goal of human genetics. This is made difficult by the effect of epistatic interactions and the need to analyze datasets with high-dimensional feature spaces. Many classification methods have been applied to this problem, one of the more recent being Support Vector Machines (SVM). Selection of which features to include in the SVM model and what parameters or kernels to use can often be a difficult task. This work uses Grammatical Evolution (GE) as a way to choose features and parameters. Initial results look promising and encourage further development and testing of this new approach.

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Cited By

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Wu DKarhade DPillai MJiang MHuang LLi GCho HRoach JLi YDivaris K(2021)Machine Learning and Deep Learning in Genetics and GenomicsMachine Learning in Dentistry10.1007/978-3-030-71881-7_13(163-181)Online publication date: 25-Jul-2021
https://doi.org/10.1007/978-3-030-71881-7_13
Cao XYu GRen WGuo MWang J(2019)DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reductionHuman Mutation10.1002/humu.23951Online publication date: 25-Nov-2019
https://doi.org/10.1002/humu.23951
Uppu SKrishna AGopalan R(2018)A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic DataIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.263512515:2(599-612)Online publication date: 1-Mar-2018
https://doi.org/10.1109/TCBB.2016.2635125
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Index Terms

Grammatical evolution support vector machines for predicting human genetic disease association
1. Computing methodologies
  1. Artificial intelligence

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Published In

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 07 July 2012

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Wu DKarhade DPillai MJiang MHuang LLi GCho HRoach JLi YDivaris K(2021)Machine Learning and Deep Learning in Genetics and GenomicsMachine Learning in Dentistry10.1007/978-3-030-71881-7_13(163-181)Online publication date: 25-Jul-2021
https://doi.org/10.1007/978-3-030-71881-7_13
Cao XYu GRen WGuo MWang J(2019)DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reductionHuman Mutation10.1002/humu.23951Online publication date: 25-Nov-2019
https://doi.org/10.1002/humu.23951
Uppu SKrishna AGopalan R(2018)A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic DataIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.263512515:2(599-612)Online publication date: 1-Mar-2018
https://doi.org/10.1109/TCBB.2016.2635125
Uppu SKrishna A(2017)Tuning Hyperparameters for Gene Interaction Models in Genome-Wide Association StudiesNeural Information Processing10.1007/978-3-319-70139-4_80(791-801)Online publication date: 29-Oct-2017
https://doi.org/10.1007/978-3-319-70139-4_80
Uppu SKrishna AGopalan R(2016)A Deep Learning Approach to Detect SNP InteractionsJournal of Software10.17706/jsw.11.10.965-97511:10(965-975)Online publication date: Oct-2016
https://doi.org/10.17706/jsw.11.10.965-975

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PAC-Bayes bounds for twin support vector machines

Multitask centroid twin support vector machines

An overview on nonparallel hyperplane support vector machine algorithms

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