Robust Prediction-Based Analysis for Genome-Wide Association and Expression Studies
K. Koppula, Skanda
MetadataShow full item record
CitationK. Koppula, Skanda, Amin Zollanvari, Ning An, and Gil Alterovitz. 2013. “Robust Prediction-Based Analysis for Genome-Wide Association and Expression Studies.” AMIA Summits on Translational Science Proceedings 2013 (1): 104.
AbstractHere we describe a prediction-based framework to analyze omic data and generate models for both disease diagnosis and identification of cellular pathways which are significant in complex diseases. Our framework differs from previous analysis in its use of underlying biology (cellular pathways/gene-sets) to produce predictive feature-disease models. In our study of alcoholism, lung cancer, and schizophrenia, we demonstrate the framework’s ability to robustly analyze omic data of multiple types and sources, identify significant features sets, and produce accurate predictive models.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11879410
- HMS Scholarly Articles