ArticleJournal of translational medicine2017
Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.
Article in Journal of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 2025Article
- Hypothesizing mechanistic links between microbes and disease using knowledge graphs.Scientific reports · 2025Article
- Optimizing transformer-based prediction of human microbe-disease associations through integrated loss strategies.PeerJ. Computer science · 2025Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.Briefings in bioinformatics · 2024Article
- GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.Scientific reports · 2024Article
- The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations.Microorganisms · 2024Review
- Microbe-disease associations prediction by graph regularized non-negative matrix factorization withJournal of cellular and molecular medicine · 2024Article
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.Journal of cellular and molecular medicine · 2024Article
- Causal effect of gut microbiota on pancreatic cancer: A Mendelian randomization and colocalization study.Journal of cellular and molecular medicine · 2024Article
- MetagenomicKG: a knowledge graph for metagenomic applications.bioRxiv : the preprint server for biology · 2024Article
- Leveraging pre-trained language models for mining microbiome-disease relationships.BMC bioinformatics · 2023Article
- 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
- MADGAN:A microbe-disease association prediction model based on generative adversarial networks.Frontiers in microbiology · 2023Article
- Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion.Frontiers in pharmacology · 2023Article
- MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec.Frontiers in microbiology · 2023Article
- A weighted non-negative matrix factorization approach to predict potential associations between drug and disease.Journal of translational medicine · 2022Article
- MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.Interdisciplinary sciences, computational life sciences · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAccumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale.
methodsIn this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity.
resultsTo evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa.
conclusionsCompared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://github.com/yahuang1991/NGRHMDA .
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