Detecting disease associated genes and gene-gene interactions with penalized AUC maximization

Zhenqiu Liu

doi:10.1109/ICMLA.2008.145

Detecting disease associated genes and gene-gene interactions with penalized AUC maximization

Zhenqiu Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In an association study, empirical evidences support the commonality of gene-gene interactions. Although genetic factors play an important role in many human diseases, multiple genes or genes and environmental factors may ultimately influence individual risk for these disease. However, such interactions are difficult to detect. In this paper, we propose a penalized area under ROC curve (AUC) maximization (LpAUC) to detect gene-gene interactions. The proposed approach is demonstrated by a simulation study and real data analysis. Analyses of both real data and simulated data show the effectiveness of our approach.

Original language	English (US)
Title of host publication	Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages	599-603
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLA.2008.145
State	Published - 2008
Event	7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States Duration: Dec 11 2008 → Dec 13 2008

Publication series

Name	Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Other

Other	7th International Conference on Machine Learning and Applications, ICMLA 2008
Country/Territory	United States
City	San Diego, CA
Period	12/11/08 → 12/13/08

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Software

Access to Document

10.1109/ICMLA.2008.145

Cite this

Liu, Z. (2008). Detecting disease associated genes and gene-gene interactions with penalized AUC maximization. In Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008 (pp. 599-603). Article 4725036 (Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008). https://doi.org/10.1109/ICMLA.2008.145

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title = "Detecting disease associated genes and gene-gene interactions with penalized AUC maximization",

abstract = "In an association study, empirical evidences support the commonality of gene-gene interactions. Although genetic factors play an important role in many human diseases, multiple genes or genes and environmental factors may ultimately influence individual risk for these disease. However, such interactions are difficult to detect. In this paper, we propose a penalized area under ROC curve (AUC) maximization (LpAUC) to detect gene-gene interactions. The proposed approach is demonstrated by a simulation study and real data analysis. Analyses of both real data and simulated data show the effectiveness of our approach.",

author = "Zhenqiu Liu",

year = "2008",

doi = "10.1109/ICMLA.2008.145",

language = "English (US)",

isbn = "9780769534954",

series = "Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008",

pages = "599--603",

booktitle = "Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008",

note = "7th International Conference on Machine Learning and Applications, ICMLA 2008 ; Conference date: 11-12-2008 Through 13-12-2008",

}

Liu, Z 2008, Detecting disease associated genes and gene-gene interactions with penalized AUC maximization. in Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008., 4725036, Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008, pp. 599-603, 7th International Conference on Machine Learning and Applications, ICMLA 2008, San Diego, CA, United States, 12/11/08. https://doi.org/10.1109/ICMLA.2008.145

Detecting disease associated genes and gene-gene interactions with penalized AUC maximization. / Liu, Zhenqiu.
Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008. 2008. p. 599-603 4725036 (Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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N2 - In an association study, empirical evidences support the commonality of gene-gene interactions. Although genetic factors play an important role in many human diseases, multiple genes or genes and environmental factors may ultimately influence individual risk for these disease. However, such interactions are difficult to detect. In this paper, we propose a penalized area under ROC curve (AUC) maximization (LpAUC) to detect gene-gene interactions. The proposed approach is demonstrated by a simulation study and real data analysis. Analyses of both real data and simulated data show the effectiveness of our approach.

AB - In an association study, empirical evidences support the commonality of gene-gene interactions. Although genetic factors play an important role in many human diseases, multiple genes or genes and environmental factors may ultimately influence individual risk for these disease. However, such interactions are difficult to detect. In this paper, we propose a penalized area under ROC curve (AUC) maximization (LpAUC) to detect gene-gene interactions. The proposed approach is demonstrated by a simulation study and real data analysis. Analyses of both real data and simulated data show the effectiveness of our approach.

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UR - https://www.scopus.com/pages/publications/60649088245#tab=citedBy

U2 - 10.1109/ICMLA.2008.145

DO - 10.1109/ICMLA.2008.145

M3 - Conference contribution

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SN - 9780769534954

T3 - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

SP - 599

EP - 603

BT - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

T2 - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Y2 - 11 December 2008 through 13 December 2008

ER -

Detecting disease associated genes and gene-gene interactions with penalized AUC maximization

Abstract

Publication series

Other

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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