Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information
IntroductionProtein–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, l...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1565860/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850250832779411456 |
|---|---|
| author | Senyu Zhou Jian Luo Mei Tang Chaojun Li Yang Li Wenhua He |
| author_facet | Senyu Zhou Jian Luo Mei Tang Chaojun Li Yang Li Wenhua He |
| author_sort | Senyu Zhou |
| collection | DOAJ |
| description | IntroductionProtein–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.MethodsIn 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.ResultsOur 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.DiscussionAdditionally, 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. |
| format | Article |
| id | doaj-art-dfbaf48dbdb74080bd34cd4b29f4c845 |
| institution | OA Journals |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-dfbaf48dbdb74080bd34cd4b29f4c8452025-08-20T01:58:04ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-03-011610.3389/fphar.2025.15658601565860Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary informationSenyu Zhou0Jian Luo1Mei Tang2Chaojun Li3Yang Li4Wenhua He5Cardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, ChinaCardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, ChinaCardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, ChinaCardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaCardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, ChinaIntroductionProtein–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.MethodsIn 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.ResultsOur 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.DiscussionAdditionally, 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.https://www.frontiersin.org/articles/10.3389/fphar.2025.1565860/fullprotein–protein interactionscardiovascular diseasedeep denoising autoencoderCatBoostevolutionary information |
| spellingShingle | Senyu Zhou Jian Luo Mei Tang Chaojun Li Yang Li Wenhua He Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information Frontiers in Pharmacology protein–protein interactions cardiovascular disease deep denoising autoencoder CatBoost evolutionary information |
| title | Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| title_full | Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| title_fullStr | Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| title_full_unstemmed | Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| title_short | Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| title_sort | predicting protein protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information |
| topic | protein–protein interactions cardiovascular disease deep denoising autoencoder CatBoost evolutionary information |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1565860/full |
| work_keys_str_mv | AT senyuzhou predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation AT jianluo predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation AT meitang predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation AT chaojunli predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation AT yangli predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation AT wenhuahe predictingproteinproteininteractionsinmicrobesassociatedwithcardiovasculardiseasesusingdeepdenoisingautoencodersandevolutionaryinformation |