Abstract
Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.