ArticleFrontiers in bioengineering and biotechnology2020
Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.Frontiers in genetics · 2026Article
- Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.Scientific reports · 2025Article
- Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning.Frontiers in genetics · 2025Article
- BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders.Frontiers in genetics · 2025Article
- NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network.Scientific reports · 2024Article
- A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.Frontiers in microbiology · 2024Article
- In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy.Gut pathogens · 2023Review
- MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation.Frontiers in microbiology · 2023Article
- GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder.BMC bioinformatics · 2022Article
- Recent advances in machine learning methods for predicting LncRNA and disease associations.Frontiers in cellular and infection microbiology · 2022Review
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Authors and funding
4 authors.
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Abstract
Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding the pathogenesis of diseases and thus provide novel strategies for the treatment, diagnosis, and prevention of diseases. To date, many computational models have been proposed for predicting microbe-disease associations using available similarity networks. However, these similarity networks are not effectively fused. In this study, we proposed a novel computational model based on multi-data integration and network consistency projection for Human Microbe-Disease Associations Prediction (HMDA-Pred), which fuses multiple similarity networks by a linear network fusion method. HMDA-Pred yielded AUC values of 0.9589 and 0.9361 ± 0.0037 in the experiments of leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, in case studies, 10, 8, and 10 out of the top 10 predicted microbes of asthma, colon cancer, and inflammatory bowel disease were confirmed by the literatures, respectively.
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