Bioinformatics and computational biology are playing increasingly significant roles in clinical research and medical practice, from the emergence of precision medicine to the interpretation of multi-faceted clinical record data, from real-time monitoring and analysis of individual’s health behavior to the analysis of nationwide patient data. DCMB faculty members carry out exciting research to translate theories and discoveries in computational biology and bioinformatics to inform clinical research and practice.
Our faculty members create and use bioinformatics pipelines and machine learning methods, coupled with imaging techniques, to identify regulatory variants and provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response (Athey). The goal is to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. They develop models of dynamic genetic networks during disease processes and use computational modeling to understand gene splicing (Guan). They design sensors to collect and analyze physiological signals and images and analyze such data in clinical decision support (Najarian). They also use mathematical analysis and simulation to generate experimentally-verifiable models of circadian rhythm (Forger) and apply machine learning algorithms to clinical image data and genotyping data (Ye, Najarian).