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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages599-603
Number of pages5
DOIs
StatePublished - Dec 1 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Publication series

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

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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