Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information

Protein-protein interactions (PPIs) are critical for understanding the molecular mechanisms underlying various biological processes, particularly in microbes associated with cardiovascular disease. Traditional experimental methods for detecting PPIs are often time-consuming and costly, leading to an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Frontiers in pharmacology Jg. 16; S. 1565860
Hauptverfasser: Zhou, Senyu, Luo, Jian, Tang, Mei, Li, Chaojun, Li, Yang, He, Wenhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 11.03.2025
Schlagworte:
cardiovascular disease
CatBoost
deep denoising autoencoder
evolutionary information
Pharmacology
protein–protein interactions
protein–protein interactions
cardiovascular disease
evolutionary information
deep denoising autoencoder
CatBoost
ISSN:1663-9812, 1663-9812
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Protein-protein interactions (PPIs) are critical for understanding the molecular mechanisms underlying various biological processes, particularly in microbes associated with cardiovascular disease. Traditional experimental methods for detecting PPIs are often time-consuming and costly, leading to an urgent need for reliable computational approaches. In this study, we present a novel model, the deep denoising autoencoder for protein-protein interaction (DAEPPI), which leverages the denoising autoencoder and the CatBoost algorithm to predict PPIs from the evolutionary information of protein sequences. Our extensive experiments demonstrate the effectiveness of the DAEPPI model, achieving average prediction accuracies of 97.85% and 98.49% on yeast and human datasets, respectively. Comparative analyses with existing effective methods further validate the robustness and reliability of our model in predicting PPIs. Additionally, we explore the application of DAEPPI in the context of cardiovascular disease, showcasing its potential to uncover significant interactions that could contribute to the understanding of disease mechanisms. Our findings indicate that DAEPPI is a powerful tool for advancing research in proteomics and could play a pivotal role in the identification of novel therapeutic targets in cardiovascular disease.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Lei Wang, Chinese Academy of Sciences (CAS), China
Leon Wong, Shenzhen Technology University, China
Reviewed by: Bo-Ya Ji, Hunan University, China
ISSN:1663-9812
1663-9812
DOI:10.3389/fphar.2025.1565860