As part of the Yale Center for Neuroinflammation, our focus is on diseases of the central nervous system, including immune dysregulation, neurodegeneration, motor system diseases, and cancer. We develop new techniques in computational pathology in order to better understand and diagnose diseases. This involves the application of machine learning, image analysis, and statistics to histologic, genomic, clinical, and physiologic data. Our overall goal is to better characterize and classify the pathologic populations of central nervous system cells, and their interactions with their local microenvionment, to understand pathways implicated in disease and to uncover new therapeutic targets.
We develop software that analyzes genomic data in concert with histologic images taken from the kinds of slides produced in the routine clinical evaluation of tissue. By using statistical and machine learning techniques, these algorithms look for patterns in cell placement and morphology that correspond to the tissue genetic profiles. We believe this simultaneous genotypic and phenotypic characterization of tissue will provide a deeper understanding key microenvironments whose interactions advance our explanations and predictions of overall disease behavior and treatment response.