Relatively little is known about the RNA-level (post-transcriptional) regulation in diverse diseases partly due to the lack of computational models that can decode the complex interactions. We develop network modeling combined with statistical learning models to elucidate the novel roles of the complex interactions.
Sepsis, defined by suspected infection and development of organ failure contributes to 1 of 5 deaths globally, with the majority of these deaths observed in infants and children. In the collaboration network from 24 hospitals across the nation, we develop 'precision medicine' strategies to treat sepsis by developing machine-learning and causal inference methods.
PROJECT WEBSITEAlthough cancer immunotherapies provide long-term clinical benefits in general, many patients still do not get benefits from the current therapy regimens. In collaboration with the melanoma program leader at the Hillman Cancer Center, we develop bioinformatic methods that analyze single-cell multi-omics data to improve the efficacy of the therapies.