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

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

doi:10.1007/978-3-319-03680-9_9

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

2 Citations (Scopus)

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) 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 language	English
Title of host publication	AI 2013
Subtitle of host publication	Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings
Publisher	Springer
Pages	80-85
Number of pages	6
ISBN (Print)	9783319036793
DOIs	https://doi.org/10.1007/978-3-319-03680-9_9
Publication status	Published - 2013
Externally published	Yes
Event	Australasian Joint Conference on Artificial Intelligence 2013 - Dunedin, New Zealand Duration: 1 Dec 2013 → 6 Dec 2013 Conference number: 26th https://link.springer.com/book/10.1007/978-3-319-03680-9 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8272
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Australasian Joint Conference on Artificial Intelligence 2013
Abbreviated title	AI 2013
Country/Territory	New Zealand
City	Dunedin
Period	1/12/13 → 6/12/13
Internet address	https://link.springer.com/book/10.1007/978-3-319-03680-9 (Proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1007/978-3-319-03680-9_9

Cite this

@inproceedings{2a390b3adfcf4ef583ae45210938c951,

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) 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.",

author = "Enes Makalic and Schmidt, \{Daniel F.\} and Hopper, \{John L.\}",

year = "2013",

doi = "10.1007/978-3-319-03680-9\_9",

language = "English",

isbn = "9783319036793",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

pages = "80--85",

booktitle = "AI 2013",

address = "Switzerland",

note = "Australasian Joint Conference on Artificial Intelligence 2013, AI 2013 ; Conference date: 01-12-2013 Through 06-12-2013",

url = "https://link.springer.com/book/10.1007/978-3-319-03680-9",

}

Makalic, E , Schmidt, DF & Hopper, JL 2013, DEPTH: a novel algorithm for feature ranking with application to genome-wide association studies. in AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science , vol. 8272 , Springer, pp. 80-85, Australasian Joint Conference on Artificial Intelligence 2013, Dunedin, New Zealand, 1/12/13. https://doi.org/10.1007/978-3-319-03680-9_9

DEPTH: a novel algorithm for feature ranking with application to genome-wide association studies. / Makalic, Enes ; Schmidt, Daniel F.; Hopper, John L.
AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. Springer, 2013. p. 80-85 (Lecture Notes in Computer Science ; Vol. 8272 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - DEPTH

T2 - Australasian Joint Conference on Artificial Intelligence 2013

AU - Makalic, Enes

AU - Schmidt, Daniel F.

AU - Hopper, John L.

N1 - Conference code: 26th

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/84893730563

U2 - 10.1007/978-3-319-03680-9_9

DO - 10.1007/978-3-319-03680-9_9

M3 - Conference Paper

AN - SCOPUS:84893730563

SN - 9783319036793

T3 - Lecture Notes in Computer Science

SP - 80

EP - 85

BT - AI 2013

PB - Springer

Y2 - 1 December 2013 through 6 December 2013

ER -

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

Abstract

Publication series

Conference

UN SDGs

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