<p>E-SNPs&GO: Embedding of protein sequence and function improves the prediction of human pathogenic variants</p>

Matteo Manfredi; Castrense Savojardo; Pier Luigi Martelli; Rita Casadio

doi:10.7490/f1000research.1119212.1

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Manfredi M, Savojardo C, Martelli PL and Casadio R. E-SNPs&GO: Embedding of protein sequence and function improves the prediction of human pathogenic variants [version 1; not peer reviewed]. F1000Research 2022, 11:1326 (poster) (https://doi.org/10.7490/f1000research.1119212.1)

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E-SNPs&GO: Embedding of protein sequence and function improves the prediction of human pathogenic variants

Matteo Manfredi, Castrense Savojardo, Pier Luigi Martelli¹, Rita Casadio

Published 16 Nov 2022 (https://doi.org/10.7490/f1000research.1119212.1)

Author Affiliations

¹ University of Bologna, Italy

Copyright © 2022 Manfredi M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 16 Nov 2022

E-SNPs&GO: Embedding of protein sequence and function improves the prediction of human pathogenic variants
[version 1; not peer reviewed]

Matteo Manfredi, Castrense Savojardo, Pier Luigi Martelli¹, Rita Casadio

Author Affiliations

¹ University of Bologna, Italy

Presented at

18th Annual Meeting of the Bioinformatics Italian Society, 2022

European Conference on Computational Biology (ECCB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Machine Learning, Deep Learning, Single Residue Variations, Single Amino-Acid Variations, Gene Ontology, Prediction

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