I am a bioinformatics-trained computational scientist interested in unraveling the underlying causes and mechanisms of drug toxicity. My research focuses on integrating high-dimensional computational approaches with systems biology knowledgebase to accelerate the discovery of novel drug-toxicity relationships buried in heterogeneous big data. I developed a novel platform, AERSMine, to mine the clinical responses of millions of patients to all FDA-approved drugs in order to identify unexpected clinical harm, benefits and alternative treatment choices for individual patients. AERSMine provides an insight into sub-population-specific differential therapeutic risks, and creates an avenue to improve our understanding of the molecular basis of adverse drug reactions.

I am also a member of the Children’s Oncology Group and am leading the effort to delineate differential treatment- and age-specific toxicity profiles within pediatric and young adult cancer patients across multiple studies and disease groups. I am designing computational approaches that facilitate effective analysis of large-scale datasets including clinical trials so we can identify a) the true regimen-specific differential risks associated with chemotherapy, b) the underlying genetic factors that drive exacerbation of toxicities, and c) recognize effective personalized therapeutic strategies for individuals at highest risk of complications.

My group is developing integrative analytical approaches that combine machine learning techniques with toxicity data, genotype-phenotype relationships, and gene-regulatory mechanisms, to help facilitate modelling novel and effective therapeutics.