Computational & Systems Biology Program
The Quaid Morris Lab
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.
S Sharma, S Kajjo, Z Harra, B Hasaj, V Delisle, D Ray, RL Gutierrez, …Uncovering a mammalian neural-specific poly (A) binding protein with unique properties. Genes & Development 2023
E Kuzmin, TM Baker, T Lesluyes, J Monlong, KT Abe, PP Coelho, … Evolution of chromosome arm aberrations in breast cancer through genetic network rewiring. bioRxiv, 2023.06. 10.544434
M Darmofal, S Suman, G Atwal, JF Chen, A Varghese, JC Chang, … Deep-learning model for tumor type classification enables enhanced clinical decision support in cancer diagnosis. Cancer Research 83 (7_Supplement), 5440-5440 2023
O Lyudovyk, Y Elhanati, A Streltsov, Q Morris, S Vardhana, B Greenbaum T-cell mediated response to emerging COVID-19 strains in patients with cancer studied via deep learning. Cancer Research 83 (7_Supplement), 795-795 2023
D Ray, KU Laverty, A Jolma, K Nie, R Samson, SE Pour, CL Tam, … RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific. Scientific Reports 13 (1), 5238 2023
Quaid Morris, PhD
- Computational 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.
- PhD, Massachusetts Institute of Technology
- [email protected]
- Email Address
- 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)
Lab News & Events
Memorial Sloan Kettering Cancer Center
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New York, NY 10065
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Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.
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Quaid Morris discloses the following relationships and financial interests:
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