Work

Seeking gene relationships in gene expression data using support vector machine regression

Year: 2007

Type: article

Abstract: Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data.

Source: BMC Proceedings

DOI: 10.1186/1753-6561-1-s1-s51

Authors: Robert Yu, Kevin DeHoff, Christopher I Amos, Sanjay Shete

Institution: The University of Texas MD Anderson Cancer Center

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funder: National Cancer Institute