MEDIA INQUIRIES
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Each protein not only has a unique sequence of amino acids — it also has a specific shape that determines its function. When protein folding goes awry, diseases like Alzheimer’s and Parkinson’s can occur. In recent years, researchers have made significant progress harnessing machine learning to predict protein folding, holding huge potential for drug development. However, current methods are limited in their ability to decipher the structure of particularly large or disordered proteins, or accurately predict protein binding. Our researchers are developing geometric deep learning methods to address this gap.
Jain, A., Charpignon, M.-L., Chen, I. Y., Philippakis, A., & Alaa, A.
(
2024
).
Generating new drug repurposing hypotheses using disease-specific hypergraphs.
Proceedings of the Pacific Symposium on Biocomputing 2024.
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https://doi.org/10.1142/9789811286421_0021Jin, W., Sarkizova, S., Chen, X., Hacohen, N., & Uhler, C.
(
2023
).
Unsupervised protein-ligand binding energy prediction via neural Euler’s rotation equation.
Advances in Neural Information Processing Systems (NeurIPS 2023).
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https://doi.org/10.48550/arXiv.2301.10814Parres-Gold, J., Levine, M., Emert, B., Stuart, A., & Elowitz, M. B.
(
2023
).
Principles of computation by competitive protein dimerization networks.
bioRxiv.
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).
https://doi.org/10.1101/2023.10.30.564854Radhakrishnan, A., Ruiz Luyten, M., Prasad, N., & Uhler, C.
(
2023
).
Transfer learning with kernel methods.
Nature Communications
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14
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1
).
https://doi.org/10.1038/s41467-023-41215-8
