Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion.
%0 Journal Article
%1 Wang2015Kernel
%A Wang, Xuefeng
%A Xing, Eric P.
%A Schaid, Daniel J.
%D 2015
%I Oxford University Press
%J Briefings in Bioinformatics
%K big-data genomics machine-learning
%N 2
%P 183--192
%R 10.1093/bib/bbu024
%T Kernel methods for large-scale genomic data analysis
%U http://dx.doi.org/10.1093/bib/bbu024
%V 16
%X Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion.
@article{Wang2015Kernel,
abstract = {Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Wang, Xuefeng and Xing, Eric P. and Schaid, Daniel J.},
biburl = {https://www.bibsonomy.org/bibtex/26bb7c4e149489caad0bc4847d782de85/karthikraman},
citeulike-article-id = {13383360},
citeulike-linkout-0 = {http://dx.doi.org/10.1093/bib/bbu024},
citeulike-linkout-1 = {http://bib.oxfordjournals.org/content/16/2/183.abstract},
citeulike-linkout-2 = {http://bib.oxfordjournals.org/content/16/2/183.full.pdf},
citeulike-linkout-3 = {http://bib.oxfordjournals.org/cgi/content/abstract/16/2/183},
citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/25053743},
citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=25053743},
day = 01,
doi = {10.1093/bib/bbu024},
interhash = {32e18fb5d3bd3b39c3b5a311b878f7b6},
intrahash = {6bb7c4e149489caad0bc4847d782de85},
issn = {1477-4054},
journal = {Briefings in Bioinformatics},
keywords = {big-data genomics machine-learning},
month = mar,
number = 2,
pages = {183--192},
pmid = {25053743},
posted-at = {2015-04-15 12:56:20},
priority = {2},
publisher = {Oxford University Press},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Kernel methods for large-scale genomic data analysis},
url = {http://dx.doi.org/10.1093/bib/bbu024},
volume = 16,
year = 2015
}