TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms
Author:
Affiliation:
1. Vision, Learning & Control Group, University of Southampton , Southampton SO17 1BJ, UK
2. Biological Sciences, University of Southampton , Southampton SO17 1BJ, UK
Abstract
Funder
Engineering and Physical Sciences Research Council (EPSRC) via the University of Southampton
EPSRC grant ‘Artificial and Augmented Intelligence for Automated Scientific Discovery
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Link
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac104/42739199/btac104.pdf
Reference58 articles.
1. Gene ontology: tool for the unification of biology;Ashburner;Nat. Genet,2000
2. A new feature vector based on gene ontology terms for protein-protein interaction prediction;Bandyopadhyay;IEEE/ACM Trans. Comput. Biol. Bioinform,2017
3. Learning the protein language: evolution, structure, and function;Bepler;Cell Syst,2021
4. Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale;Cafarelli;Curr. Opin. Struct. Biol,2017
5. TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments;Chen;Bioinformatics,2020
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