Work

Kernel methods for large-scale genomic data analysis

Year: 2014

Type: review

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.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbu024

Authors: X. Wang, E. P. Xing, D. J. Schaid

Institutions: Stony Brook University, Carnegie Mellon University, Mayo Clinic

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Institutes of Health, National Institute of General Medical Sciences

Awards: G1237202219, Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for Studyin, Quantitative Methods for Genetic Epidemiology, Genome-Transcription-Phenome-Wide Association: a new paradigm for association stu