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Ashwin Adrian Kallor on Muck Rack

Ashwin Adrian Kallor

As seen in: Nature
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Articles

Artificial intelligence perceives marginal gains from MHC class I haplotype data in antigen presentation predictions

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By Fabio Massimo Zanzotto, Aria Nourbakhsh, Piyush Borole, Emilia Daghir-Wojtkowiak, Davide Venditti, Elena Sofia Ruzzetti, Maksym Zoziuk, Maurizio Mattei, Ajitha Rajan, Christophe Battail, Javier Alfaro, Michele Mastromattei, Aleksander Palkowski, Ashwin Adrian Kallor, Michał Waleron, Roberta Bernadini, Dimitri Koroliouk
| Nature Verified
Abstract Artificial Intelligence offers valuable tools for scientific discovery, but when used improperly, it can cause blunders. In this paper, we report findings related to the role of Major Histocompatibility Complexes (MHCs) in epitope prediction. Through a serendipitous programmatic error, we observed that methods like TransPHLA yield similar results on both training and testing datasets when only peptides are used, without knowing the specifics of MHC alleles.
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