Title :
Detecting Disease Associated Genes and Gene-Gene Interactions with Penalized AUC Maximization
Author_Institution :
Greenebaum Cancer Center, Univ. of Maryland, Baltimore, MD
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.
Keywords :
data mining; diseases; environmental factors; genetics; medical computing; optimisation; area under ROC curve maximization; data analysis; disease associated genes; environmental factors; gene-gene interactions; human disease; penalized AUC maximization; Analytical models; Cancer; Diseases; Environmental factors; Genetics; Input variables; Logistics; Machine learning; Support vector machines; Testing;
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
DOI :
10.1109/ICMLA.2008.145