Network-Based Penalized Regression With Application to Genomic Data

Penalized regression approaches are attractive in dealing with high-dimensional data such as arising in high-throughput genomic studies. New methods have been introduced to utilize the network structure of predictors, for example, gene networks, to improve parameter estimation and variable selection...

Full description

Saved in:

Bibliographic Details
Published in:	Biometrics Vol. 69; no. 3; pp. 582 - 593
Main Authors:	Kim, Sunkyung, Pan, Wei, Shen, Xiaotong
Format:	Journal Article
Language:	English
Published:	England Blackwell Publishing Ltd 01.09.2013 International Biometric Society
Subjects:	algorithms BIOMETRIC METHODOLOGY Biometry - methods breast neoplasms Breast Neoplasms - genetics Data analysis data collection Databases, Genetic - statistics & numerical data Female Gene Expression Gene Regulatory Networks genes Genes, BRCA1 Genes, BRCA2 Genes, p53 Genomics Genomics - statistics & numerical data High-Throughput Nucleotide Sequencing - statistics & numerical data Humans Models, Statistical Networks analysis Nonconvex minimization Penalty Regression Analysis Truncated Lasso penalty Gene expression Truncated Lasso penalty Networks analysis Penalty Nonconvex minimization
ISSN:	0006-341X, 1541-0420, 1541-0420
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!