ArticlePloS one2017
A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 74 citations in OpenAlex.
- Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction.Journal of pharmaceutical analysis · 2025Article
- A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships.BMC medical informatics and decision making · 2025Article
- Global burden of lower respiratory infections attributable to cytomegalovirus, 1990-2021: a systematic analysis from the MICROBE database.Frontiers in microbiology · 2025Article
- Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery.Frontiers in pharmacology · 2025Article
- SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment.BMC genomics · 2024Article
- Predicting disease-associated microbes based on similarity fusion and deep learning.Briefings in bioinformatics · 2024Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Microbe-disease associations prediction by graph regularized non-negative matrix factorization withJournal of cellular and molecular medicine · 2024Article
- ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations.Journal of cellular and molecular medicine · 2024Article
- MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.Frontiers in microbiology · 2024Article
- Predicting microbe-disease associations via graph neural network and contrastive learning.Frontiers in microbiology · 2024Article
- Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion.BMC genomics · 2023Article
- Predicting potential microbe-disease associations based on auto-encoder and graph convolution network.BMC bioinformatics · 2023Article
- GMMAD: a comprehensive database of human gut microbial metabolite associations with diseases.BMC genomics · 2023Article
- In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy.Gut pathogens · 2023Review
- SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM.Frontiers in microbiology · 2023Article
- A novel bi-directional heterogeneous network selection method for disease and microbial association prediction.BMC bioinformatics · 2022Article
- Graph neural network and multi-data heterogeneous networks for microbe-disease prediction.Frontiers in microbiology · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
- MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.Journal of translational medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the microbiome has a significant impact on human health and disease, microbe-disease associations can be utilized as a valuable resource for understanding disease pathogenesis and promoting disease diagnosis and prognosis. Accordingly, it is necessary for researchers to achieve a comprehensive and deep understanding of the associations between microbes and diseases. Nevertheless, to date, little work has been achieved in implementing novel human microbe-disease association prediction models. In this paper, we develop a novel computational model to predict potential microbe-disease associations by bi-random walk on the heterogeneous network (BiRWHMDA). The heterogeneous network was constructed by connecting the microbe similarity network and the disease similarity network via known microbe-disease associations. Microbe similarity and disease similarity were calculated by the Gaussian interaction profile kernel similarity measure; moreover, a logistic function was applied to regulate disease similarity. Additionally, leave-one-out cross validation and 5-fold cross validation were implemented to evaluate the predictive performance of our method; both cross validation methods performed well. The leave-one-out cross validation experiment results illustrate that our method outperforms other previously proposed methods. Furthermore, case studies on asthma and inflammatory bowel disease prove the favorable performance of our method. In conclusion, our method can be considered as an effective computational model for predicting novel microbe-disease associations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.