ArticleFrontiers in microbiology2018
Human Microbe-Disease Association Prediction Based on Adaptive Boosting.
Article in Frontiers in microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 79 citations in OpenAlex.
- Heterogeneous dual-channel and interpretable graph representation learning with global virtual nodes for microRNA-mediated drug sensitivity prediction.Molecular diversity · 2026Article
- Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.Scientific reports · 2025Article
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- 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
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery.Frontiers in pharmacology · 2025Article
- Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.Briefings in bioinformatics · 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
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.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 association based on graph autoencoder and inductive matrix completion with multi-similarities fusion.Frontiers in microbiology · 2024Article
- 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
- 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
- A novel bi-directional heterogeneous network selection method for disease and microbial association prediction.BMC bioinformatics · 2022Article
- NNAN: Nearest Neighbor Attention Network to Predict Drug-Microbe Associations.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What's more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA.
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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.