Our lab’s research involves designing algorithms that leverage biological networks to connect different types of experimental data and detect surprising relationships among them. We use such techniques to study human disease, in particular cancer and viral infection. We also focus on the dynamic behaviors of biological networks and develop techniques to reconstruct dynamic models of signaling pathways and transcriptional regulatory networks from high-throughput proteomic and transcriptomic data.
Updates from the Lab
Our study on classifying T cell activity with convolutional neural networks has been posted as a bioRxiv preprint.
Our manuscript on selecting chemicals to characterize new kinase targets has been published.
Our manuscript Open collaborative writing with Manubot has been published. See the Manubot website to learn more about our GitHub-based writing platform and try it yourself.