DEPTH: a novel algorithm for feature ranking with application to genome-wide association studies

Enes Makalic, Daniel F. Schmidt, John L. Hopper

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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) applicable to any parametric regression task, (ii) designed to be run in a parallel environment, and (iii) adapts 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.

Original languageEnglish
Title of host publicationAI 2013
Subtitle of host publicationAdvances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings
Number of pages6
ISBN (Print)9783319036793
Publication statusPublished - 2013
Externally publishedYes
EventAustralasian Joint Conference on Artificial Intelligence 2013 - Dunedin, New Zealand
Duration: 1 Dec 20136 Dec 2013
Conference number: 26th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAustralasian Joint Conference on Artificial Intelligence 2013
Abbreviated titleAI 2013
CountryNew Zealand
Internet address

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