A Bayesian framework for knowledge driven regression model in micro-array data analysis. (1st October 2008)

• Record Type: Journal Article
• Authors: Jin, Rong; Si, Luo; Chan, Christina
• Is Part Of: International journal of data mining and bioinformatics. Volume 2:Number 3(2008)
• Page Start: 250
• Page End: 267
• Publication Date: 2008-10-01
• Subjects: Bayesian analysis -- knowledge driven data regression -- graph Laplacian -- gene expression data -- data mining -- bioinformatics -- sparse data -- linear regression models -- pair wise similarity -- micro-array data analysis -- gene ontology; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

This paper addresses the sparse data problem in the linear regression model, namely the number of variables is significantly larger than the number of the data points for regression. We assume that in addition to the measured data points, the prior knowledge about the input variables may be provided in the form of pair wise similarity. We presented a full Bayesian framework to effectively exploit the similarity information of the input variables for linear regression. Empirical studies with gene expression data show that the regression errors can be reduced significantly by incorporating the similarity information derived from gene ontology.