Incorporating predictor network in penalized regression with application to microarray data

Biometrics

Volumn 66, Issue 2, 2010, Pages 474-484

  Pan, Wei ^a   Xie, Benhuai ^a   Shen, Xiaotong ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Elastic net; Generalized boosted Lasso; L1 penalization; Laplacian; Lasso; Microarray gene expression; Penalized likelihood

Indexed keywords

GENE EXPRESSION; SAMPLING;

ELASTIC NET; GENERALIZED BOOSTED LASSO; L1 PENALIZATION; LAPLACIANS; LASSO; MICROARRAY GENE EXPRESSION; PENALISATION; PENALIZED LIKELIHOOD; PENALIZED-LIKELIHOOD; VARIABLES SELECTIONS;

REGRESSION ANALYSIS;

DATA SET; GENE EXPRESSION; GENETIC ANALYSIS; PHENOTYPE; REGRESSION ANALYSIS; SAMPLING; SIMULATION; SURVIVAL;

BIOLOGICAL MODEL; COMPUTER SIMULATION; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; GLIOBLASTOMA; HUMAN; MORTALITY; PHENOTYPE; PREDICTIVE VALUE; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; SURVIVAL RATE;

COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GLIOBLASTOMA; HUMANS; LINEAR MODELS; METHODS; MODELS, BIOLOGICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHENOTYPE; PREDICTIVE VALUE OF TESTS; SURVIVAL RATE;

EID: 77952976255     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01296.x     Document Type: Article

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