A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology

Ching Lee Koo, Mei Jing Liew, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh

Research output: Contribution to journalReview articlepeer-review

43 Citations (Scopus)

Abstract

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

Original languageEnglish
Article number432375
JournalBioMed Research International
Volume2013
DOIs
Publication statusPublished - 2013
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology

Fingerprint

Dive into the research topics of 'A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology'. Together they form a unique fingerprint.

Cite this