Kernel methods for large-scale genomic data analysis

Xuefeng Wang, Eric P. Xing, Daniel J. Schaid

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)183-192
Number of pages10
JournalBriefings in bioinformatics
Issue number2
StatePublished - Mar 1 2015


  • Association test
  • Kernel logistic regression
  • Kernel methods
  • Lasso
  • Machine learning
  • Prediction
  • Structured mapping

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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