Transfer Learning with Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans

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Title: Transfer Learning with Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans

  • authors: Jack Lanchantin, Arshdeep Sekhon, Clint Miller, Yanjun Qi

Paper BioArxiv

Talk: Slide Coming

Abstract

The novel coronavirus SARS-CoV-2, which causes Coronavirus disease 2019 (COVID-19), is a significant threat to worldwide public health. Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins that compromise normal human protein-protein interactions (PPI). Current in vivo methods to identify PPIs between a novel virus and humans are slow, costly, and difficult to cover the vast interaction space. We propose a novel deep learning architecture designed for in silico PPI prediction and a transfer learning approach to predict interactions between novel virus proteins and human proteins. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions for SARS-CoV-2, H1N1, and Ebola.

Citations

@article {Lanchantin2020.12.14.422772,
	author = {Lanchantin, Jack and Sekhon, Arshdeep and Miller, Clint and Qi, Yanjun},
	title = {Transfer Learning with MotifTransformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans},
	elocation-id = {2020.12.14.422772},
	year = {2020},
	doi = {10.1101/2020.12.14.422772},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2020/12/15/2020.12.14.422772},
	eprint = {https://www.biorxiv.org/content/early/2020/12/15/2020.12.14.422772.full.pdf},
	journal = {bioRxiv}
}

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