Paper Info
Title
Latent feature decompositions for integrative analysis of diverse high-throughput genomic data
Abstract
A general method for regressing a continuous response upon large groups of diverse genetic covariates via dimension reduction is developed and exemplified. It is shown that allowing latent features derived from different covariate groups to interact aids in prediction when interactions subsist among the original covariates. A means of selecting a subset of relevant covariates from the original set is proposed, and a simulation study is performed to demonstrate the effectiveness of the procedure for prediction and variable selection. The procedure is applied to a high-dimensional lung cancer data set to model the effects of gene expression, copy number variation, and methylation on a drug response.
Year
DOI
Venue
2012
10.1109/GENSIPS.2012.6507746
Genomic Signal Processing and Statistics,
Keywords
Field
DocType
bioinformatics,cancer,data reduction,drugs,genetics,genomics,lung,continuous response regression,copy number variation effects,covariate groups,dimension reduction,diverse genetic covariates,diverse high throughput genomic data,drug response,gene expression effects,high dimensional lung cancer data set,integrative analysis,latent feature decomposition,methylation effects
Covariate,Dimensionality reduction,Bayesian inference,Glioblastoma,Feature selection,Copy-number variation,Computer science,Graphical model,Bioinformatics,Throughput
Conference
ISSN
ISBN
Citations 
2150-3001
978-1-4673-5234-5
4
PageRank 
References 
Authors
0.51
2
10
Name
Order
Citations
PageRank
Karl B. Gregory170.96
Kevin Coombes229456.27
Amin Momin370.96
Luc Girard491.32
Lauren A. Byers540.51
Steven Lin6618.28
Michael Peyton740.51
John V. Heymach840.51
John D. Minna9302.67
Veerabhadran Baladandayuthapani108811.81