Computational & Systems Biology Program
The Quaid Morris Lab
Research
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.
Publications Highlights
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
People
Quaid Morris, PhD
Member
- 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
Members
Lab Alumni
Lab Affiliations
Achievements
- 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)
Open Positions
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Career Opportunities
Get in Touch
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Lab Head Email
Disclosures
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.
MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.
Quaid Morris discloses the following relationships and financial interests:
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Microsoft Corporation
Equity
The information published here is a complement to other publicly reported data and is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.
This page and data include information for a specific MSK annual disclosure period (January 1, 2023 through disclosure submission in spring 2024). This data reflects interests that may or may not still exist. This data is updated annually.
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