Bayesian variable selection for linear regression in high dimensional microarray data

Authors:
Wellington Cabrera;Carlos Ordonez;David Sergio Matusevich;Veerabhadran Baladandayuthapani
Affiliations:
University of Houston, Houston, TX, USA;University of Houston, Houston, TX, USA;University of Houston, Houston, TX, USA;University of Texas, Houston, TX, USA
Venue:
Proceedings of the 7th international workshop on Data and text mining in biomedical informatics
Year:
2013

Citing 3
Cited 1

An introduction to variable and feature selection

The Journal of Machine Learning Research
Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Bayesian Core: A Practical Approach to Computational Bayesian Statistics
Statistical Model Computation with UDFs

IEEE Transactions on Knowledge and Data Engineering

DTMBIO 2013: international workshop on data and text mining in biomedical informatics

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Variable selection is a fundamental problem in Bayesian statistics whose solution requires exploring a combinatorial search space. We study the solution of variable selection with a well-known MCMC method, which requires thousands of iterations. We present several algorithmic optimizations to accelerate the MCMC method to make it work efficiently inside a database system. Our optimizations include sufficient statistics, variable preselection, hash tables and calling a linear algebra library. We present experiments with very high dimensional microarray data sets to predict cancer survival time. We discuss encouraging findings, identifying specific genes likely to predict the survival time for brain cancer patients. We also show our DBMS-based algorithm is orders of magnitude faster than the R statistical package. Our work shows a DBMS is a promising platform to analyze microarray data.