FUNCTION

Achieving these goals requires developing innovative computational methods for the analysis and modeling of diverse high-throughput "big data" in biology. By integrating massive collections of heterogeneous datasets, we can extract the relevant information necessary to make precise biological predictions and computationally direct experiments. This challenging problem can only be tackled through an interdisciplinary approach. For this reason, our team includes experts in bioinformatics, machine learning, statistics, algorithms, and biology. We translate our computational predictions into testable hypotheses through close collaborations with experimental and clinical researchers in diverse areas spanning autism, Alzheimer's disease, kidney disease, and breast cancer. We aim to produce high-resolution dynamic predictive models to study the effects of genetic and environmental perturbations in cells, and ultimately whole organisms, elucidating the molecular basis of disease... We have..