An MM-based optimization algorithm for sparse linear modeling on microarray data analysis

Xiaohui Chen, Raphael Gottardo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sparsity is crucial for high-dimensional statistical modeling. On one hand, dimensionality reduction can reduce the variability of estimation and thus provide reliable predictive power. On the other hand, the selected sub-model can discover and emphasize the underlying dependencies, which is useful for objective interpretation. Many variable selection methods have been proposed in literatures. For a prominent example, Least Absolute Shrinkage and Selection Operator (lasso) in linear regression context has been extensively explored. This paper discusses a class of scaled mixture of Gaussian models from both a penalized likelihood and a Bayesian regression point of view. We propose an Majorize-Minimize (MM) algorithm to find the Maximum A Posteriori (MAP) estimator, where the EM algorithm can be stuck at local optimum for some members in this class. Simulation studies show the outperformance of proposed algorithm in nonstochastic design variable selection scenario. The proposed algorithm is applied to a real large-scale E.coli data set with known bona fide interactions for constructing sparse gene regulatory networks. We show that our regression networks with a properly chosen prior can perform comparably to state-of-the-art regulatory network construction algorithms.

Original languageEnglish (US)
Title of host publication3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs
StatePublished - 2009
Externally publishedYes
Event3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China
Duration: Jun 11 2009Jun 13 2009

Publication series

Name3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Other

Other3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
CountryChina
CityBeijing
Period6/11/096/13/09

ASJC Scopus subject areas

  • Biotechnology
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'An MM-based optimization algorithm for sparse linear modeling on microarray data analysis'. Together they form a unique fingerprint.

Cite this