title: Variable selection and dependency networks for genomewide data.
abstract: We describe a new stochastic search algorithm for linear regression models called the bounded mode stochastic search (BMSS). We make use of BMSS to perform variable selection and classification as well as to construct sparse dependency networks. Furthermore, we show how to determine genetic networks from genomewide data that involve any combination of continuous and discrete variables. We illustrate our methodology with several real-world data sets.
authors.affiliation: Department of Statistics and Department of Biobehavioral Nursing and Health Systems, University of Washington Seattle, WA 98195, USA. adobra@u.washington.edu
authors.forename: Adrian
authors.initials: A
authors.lastname: Dobra
mesh_terms: D000465:Algorithms; D056808:Biostatistics / Q000379:methods; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D005260:Female; D015870:Gene Expression; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008207:Lymphatic Metastasis; D011960:Receptors, Estrogen / Q000378:metabolism; D013269:Stochastic Processes
chemical_list: D011960:Receptors, Estrogen