An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders

Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen protei...

Full description

Saved in:
Bibliographic Details
Published in:Procedia computer science Vol. 207; pp. 2023 - 2032
Main Author: Albu, Alexandra-Ioana
Format: Journal Article
Language:English
Published: Elsevier B.V 2022
Subjects:
68T05
denoising autoencoders 2000 MSC: 92D20
protein-protein interaction prediction
supervised autoencoders
68T05
protein-protein interaction prediction
supervised autoencoders
denoising autoencoders 2000 MSC: 92D20
ISSN:1877-0509, 1877-0509
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2022.09.261