Our three primary research interests are:
The Bayesian networks software is publically available at (https://bitbucket.org/uthsph/bnomics/). We are currently working on developing an even more user-friendly version. The software is essentially aimed at the user with “big biological data”. I,e, if an investigator has a complex dataset, BNOmics can likely make sense (automated biological hypotheses generation) out of it. Over the last year, we’ve been successful in modeling in different domains, ranging from evolutionary biology to cancer epidemiology to immunogenetics.
We are predominantly interested in developing and maintaining systems biology / computational biology data analysis methodology and software (with emphasis on mathematical rigour and adaptability to heterogeneous data types) directly applicable to the large-scale heterogeneous data being routinely generated within the current biomedical research pipelines. We collaborate with both internal (COH) and external investigators in applying such methodology to the big datasets ranging from genomic to epigenomic to just about any kind of -omic. We are also interested in developing mathematical models and flexible analysis techniques in the context of molecular evolution research.