Using ontologies for hypothesis generation and their application to basic research in aging




Understanding the mechanisms and genetic contributions to human aging is one of the most important challenges to biomedical research.  It is now known that slowing the aging process also slows the onset of aging related disease such as cancer or neurodegenerative diseases. In collaboration with Dr. Nigam Shah and the NCBO,  we have been collaborating with researchers at the Buck Institute for Research on Aging for several years, and we have together built several tools to aid molecular and cellular biology research.  In this presentation, I will describe our novel approach to hypothesis generation from high throughput experiments and using concept enrichment analysis and analysis of gene or protein sets.  We are applying our tools to several collaborations in aging and aging related disease.  To that end I will provide some examples of our work in Huntington’s disease and the basic biology of aging.



Dr. Sean Mooney is a group leader in the fields of computational biology and bioinformatics, and manages an active NIH funded laboratory. He received his PhD from UCSF in 2001 under Prof. Teri Klein and did an American Cancer Society Fellowship under Prof. Russ Altman at Stanford.  In addition to collaborative activities in bioinformatics, his primary research interests focus on building and applying computational models to understand how genes and genetic variation alters phenotype or causes disease at the protein level.   He also has an interest in developing new tools for understanding high throughput experimental datasets.  His research has led to the development of bioinformatic tools for aiding in characterizing genes and genetic variation data and its effect on proteins and proteomes including MutPred, the In Silico Functional Profiling method, MutDB, S-BLEST, the Catalytic Residue predictor, and PhenoPred.