Paper Info
Title
DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies
Abstract
Variable selection is a common problem in regression modelling with a myriad of applications. This paper proposes a new feature ranking algorithm (DEPTH) for variable selection in parametric regression based on permutation statistics and stability selection. DEPTH is: (i)äapplicable to any parametric regression task, (ii)ädesigned to be run in a parallel environment, and (iii)äadapts naturally to the correlation structure of the predictors. DEPTH was applied to a genome-wide association study of breast cancer and found evidence that there are variants in a pathway of candidate genes that are associated with a common subtype of breast cancer, a finding which would not have been discovered by conventional analyses.
Year
DOI
Venue
2013
10.1007/978-3-319-03680-9_9
Australasian Conference on Artificial Intelligence
Field
DocType
Citations 
Candidate gene,Feature selection,Breast cancer,Regression,Pattern recognition,Permutation,Genome-wide association study,Algorithm,Parametric statistics,Correlation,Artificial intelligence,Mathematics
Conference
0
PageRank 
References 
Authors
0.34
1
3
Name
Order
Citations
PageRank
Enes Makalic15511.54
Daniel F. Schmidt25110.68
John L. Hopper321.75