The Shah Lab

The Shah laboratory studies ontology based approaches to annotate, index, integrate and analyze diverse information types available in biomedicine for the purpose of enabling data-driven decision making in medicine and health care. My research interest is to make biomedical information actionable. I am interested in:

1. Annotation Analytics: In order to understand the “gene lists” from analysis of high-throughput data, researchers routinely use Gene Ontology based analyses. With available methods for automated annotation and the existence of over 200 biomedical ontologies, it’s time for “big data” mining in annotation analysis. We have created over 5 billion annotations on 20 public data sources and need miners. For example, by annotating known protein mutations with disease terms, we identified a class of diseases – blood coagulation disorders – that are associated with depletion in substitutions at O-linked glycosylation sites.
2. Data driven medicine: The goal of this research is to combine machine learning and text-mining with prior knowledge encoded in medical ontologies to discover hidden trends and build risk models as well as drive data driven decision making and comparative effectiveness studies. For example, annotation analysis can identify combinations of drug classes, risk-factors and co-morbidities that are common in patients with a certain outcome—e.g. those re-admitted after transplant surgery—to provide candidate hypotheses about the possible causes as well as predictors of that outcome.

1. Socially Conscious Informatics: Clinical decision support tools traditionally focus on supporting a high trained individual—the doctor. Let’s turn decision support on its head to aid the patient and provide support on a cell phone; especially in situations where there is lack of access to a highly trained individual. A cell phone app can diagnose respiratory distress or severe dehydration in a child. Just these two conditions kill over 35% of the estimated 10.6 million child deaths each year.