Our lab uses machine learning and artificial intelligence to do biomedical research, focusing on cancer evolution, gene regulation, clinical informatics, and gene function prediction. A key interest is the role of RNA-binding proteins (RBPs) in post-transcriptional regulation. We focus on developing computational and experimental techniques to determine the RNA specificities of RBPs (both sequence and structural) and use these specificities to predict their target transcripts, determine RBP function, and ultimately decipher the regulatory code. Another focus is reconstructing and modelling somatic evolution (pre- and post-cancer) using bulk and single-cell genomic data. In general, we are focused on using large, heterogeneous functional genomic datasets to uncover insights about gene function. Recently, we have becoming increasingly interested in using artificial intelligence and predictive analytics, along with electronic medical records, to inform patient care, particularly in the domain of auto-immune disease.
Quaid Morris, PhD
Research FocusComputational biologist Quaid Morris uses artificial intelligence techniques and develops machine learning algorithms to study gene regulation, cancer evolution, clinical informatics, and other topics in systems biology.
EducationPhD, Massachusetts Institute of Technology
- Jiao, W., Atwal, G., Polak, P. et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun 11, 728 (2020).
- Ray, D., Kazan, H., Cook, K. et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature 499, 172–177 (2013).
- Clarivate Web of Science Highly Cited Research (2018-present)
- CIFAR Artificial Intelligence Chair (2018-present)
- Assigned RNA-binding preferences to >20% of metazoan RNA-binding proteins (and >10% of eukaryotic RBPs) (w/ Timothy Hughes)
- Developed GeneMANIA algorithm and website (w/ Gary Bader)