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# This CITATION.cff file was generated with cffinit. | ||
# Visit https://bit.ly/cffinit to generate yours today! | ||
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cff-version: 1.2.0 | ||
title: INTREPPPID | ||
message: ' Incorporating Triplet Error for Predicting PPIs using Deep Learning ' | ||
type: software | ||
authors: | ||
- given-names: Joseph | ||
family-names: Szymborski | ||
email: joseph.szymborski@mail.mcgill.ca | ||
affiliation: >- | ||
Department of Electrical and Computer Engineering, | ||
McGill University | ||
orcid: 'https://orcid.org/0000-0003-1559-6225' | ||
- given-names: Amin | ||
family-names: Emad | ||
email: amin.emad@mcgill.ca | ||
affiliation: >- | ||
Department of Electrical and Computer Engineering, | ||
McGill University | ||
orcid: 'https://orcid.org/0000-0002-5108-4887' | ||
identifiers: | ||
- type: doi | ||
value: 10.1093/bib/bbae405 | ||
description: Publication in Briefings in Bioinformatics | ||
- type: doi | ||
value: 10.5281/zenodo.10652231 | ||
description: Zenodo Record | ||
repository-code: 'https://github.com/Emad-COMBINE-lab/intrepppid' | ||
url: 'https://emad-combine-lab.github.io/intrepppid/' | ||
abstract: >- | ||
An overwhelming majority of protein–protein interaction | ||
(PPI) studies are conducted in a select few model | ||
organisms largely due to constraints in time and cost of | ||
the associated ‘wet lab’ experiments. In silico PPI | ||
inference methods are ideal tools to overcome these | ||
limitations, but often struggle with cross-species | ||
predictions. We present INTREPPPID, a method that | ||
incorporates orthology data using a new ‘quintuplet’ | ||
neural network, which is constructed with five parallel | ||
encoders with shared parameters. INTREPPPID incorporates | ||
both a PPI classification task and an orthologous locality | ||
task. The latter learns embeddings of orthologues that | ||
have small Euclidean distances between them and large | ||
distances between embeddings of all other proteins. | ||
INTREPPPID outperforms all other leading PPI inference | ||
methods tested on both the intraspecies and cross-species | ||
tasks using strict evaluation datasets. We show that | ||
INTREPPPID’s orthologous locality loss increases | ||
performance because of the biological relevance of the | ||
orthologue data and not due to some other specious aspect | ||
of the architecture. Finally, we introduce PPI.bio and PPI | ||
Origami, a web server interface for INTREPPPID and a | ||
software tool for creating strict evaluation datasets, | ||
respectively. Together, these two initiatives aim to make | ||
both the use and development of PPI inference tools more | ||
accessible to the community. | ||
keywords: | ||
- protein-protein interactions | ||
- orthology | ||
- cross-species | ||
- deep learning | ||
license: AGPL-3.0-or-later | ||
commit: 1fb690c | ||
version: '1' | ||
date-released: '2024-02-12' |