ArticleFrontiers in microbiology2017
PBHMDA: Path-Based Human Microbe-Disease Association Prediction.
Article in Frontiers in microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 95 citations in OpenAlex.
- Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering.Interdisciplinary sciences, computational life sciences · 2026Article
- Development and validation of embedded multilayer attention graph convolution neural network models for predicting gut microbe-disease associations.Frontiers in microbiology · 2026Article
- 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
- 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
- Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning.Interdisciplinary sciences, computational life sciences · 2024Article
- Causal effect of gut microbiota on pancreatic cancer: A Mendelian randomization and colocalization study.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 potential microbe-disease associations based on auto-encoder and graph convolution network.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
- MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.Interdisciplinary sciences, computational life sciences · 2022Article
- GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.BMC genomics · 2022Article
- Ontology-aware neural network: a general framework for pattern mining from microbiome data.Briefings in bioinformatics · 2022Article
- Graph neural network and multi-data heterogeneous networks for microbe-disease prediction.Frontiers in microbiology · 2022Article
- Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.Frontiers in microbiology · 2022Article
- A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.Scientific reports · 2021Article
- Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.Scientific reports · 2021Article
- 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
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advance of sequencing technology and microbiology, the microorganisms have been found to be closely related to various important human diseases. The increasing identification of human microbe-disease associations offers important insights into the underlying disease mechanism understanding from the perspective of human microbes, which are greatly helpful for investigating pathogenesis, promoting early diagnosis and improving precision medicine. However, the current knowledge in this domain is still limited and far from complete. Here, we present the computational model of Path-Based Human Microbe-Disease Association prediction (PBHMDA) based on the integration of known microbe-disease associations and the Gaussian interaction profile kernel similarity for microbes and diseases. A special depth-first search algorithm was implemented to traverse all possible paths between microbes and diseases for inferring the most possible disease-related microbes. As a result, PBHMDA obtained a reliable prediction performance with AUCs (The area under ROC curve) of 0.9169 and 0.8767 in the frameworks of both global and local leave-one-out cross validations, respectively. Based on 5-fold cross validation, average AUCs of 0.9082 ± 0.0061 further demonstrated the efficiency of the proposed model. For the case studies of liver cirrhosis, type 1 diabetes, and asthma, 9, 7, and 9 out of predicted microbes in the top 10 have been confirmed by previously published experimental literatures, respectively. We have publicly released the prioritized microbe-disease associations, which may help to select the most potential pairs for further guiding the experimental confirmation. In conclusion, PBHMDA may have potential to boost the discovery of novel microbe-disease associations and aid future research efforts toward microbe involvement in human disease mechanism. The code and data of PBHMDA is freely available at http://www.escience.cn/system/file?fileId=85214.
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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.