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TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms
MOTIVATION: Protein–protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exag...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363134/ https://www.ncbi.nlm.nih.gov/pubmed/35176146 http://dx.doi.org/10.1093/bioinformatics/btac104 |
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author | Ieremie, Ioan Ewing, Rob M Niranjan, Mahesan |
author_facet | Ieremie, Ioan Ewing, Rob M Niranjan, Mahesan |
author_sort | Ieremie, Ioan |
collection | PubMed |
description | MOTIVATION: Protein–protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. RESULTS: We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ieremie/TransformerGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9363134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93631342022-08-10 TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms Ieremie, Ioan Ewing, Rob M Niranjan, Mahesan Bioinformatics Original Papers MOTIVATION: Protein–protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. RESULTS: We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ieremie/TransformerGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-02-17 /pmc/articles/PMC9363134/ /pubmed/35176146 http://dx.doi.org/10.1093/bioinformatics/btac104 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Ieremie, Ioan Ewing, Rob M Niranjan, Mahesan TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title | TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title_full | TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title_fullStr | TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title_full_unstemmed | TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title_short | TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
title_sort | transformergo: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363134/ https://www.ncbi.nlm.nih.gov/pubmed/35176146 http://dx.doi.org/10.1093/bioinformatics/btac104 |
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