The application of deep learning to predicting events in the antigen processing and presentation pathway

Identification: 4013


Description

The application of deep learning to predicting events in the antigen processing and presentation pathway

Alex Rubinsteyn, Tim O'Donnell, Bulent Arman Aksoy, Nandita Damaraju, Giancarlo Kerg,

Jeff Hammerbacher

Icahn School of Medicine at Mount Sinai

Our lab has been working for several years to bring modern neural network software and methods into immunology. Our peptide/MHC class I binding affinity predictor, MHCflurry, has performed well on the IEDB’s automated benchmark since mid-2015. We’ve recently extended our work to predicting class II binding affinity as well as class I cleavage, stability, and presentation. Our code and models are open source without restrictions on commercial use. In this talk, we’ll share lessons learned from the MHCflurry project, discuss how this software can be used to answer basic and translational research questions, and outline our plans for future work in this domain.

Credits

Credits: None available.

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