title: Kernel methods for large-scale genomic data analysis.
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.
authors: Xuefeng
authors: X
authors: Wang
authors: Eric P
authors: EP
authors: Xing
authors: Daniel J
authors: DJ
authors: Schaid
mesh_terms: D019295:Computational Biology; D003627:Data Interpretation, Statistical; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D060388:Support Vector Machine
keywords: association test; kernel logistic regression; kernel methods; lasso; machine learning; prediction; structured mapping