ArticleFrontiers in microbiology2019
Identification and Analysis of Human Microbe-Disease Associations by Matrix Decomposition and Label Propagation.
Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Development and validation of embedded multilayer attention graph convolution neural network models for predicting gut microbe-disease associations.Frontiers in microbiology · 2026Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 2025Article
- 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
- 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
- A novel bi-directional heterogeneous network selection method for disease and microbial association prediction.BMC bioinformatics · 2022Article
- Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy.Frontiers in neuroscience · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
- Mining microbe-disease interactions from literature via a transfer learning model.BMC bioinformatics · 2021Article
- MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.Journal of translational medicine · 2021Article
- Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.Frontiers in genetics · 2021Article
- Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction.Frontiers in microbiology · 2021Article
- The emergence of new trends in clinical laboratory diagnosis.Saudi medical journal · 2020Review
- RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization.Frontiers in microbiology · 2020Article
- Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network.Frontiers in microbiology · 2020Article
- RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction.Frontiers in microbiology · 2019Article
- "EviMass": A Literature Evidence-Based Miner for Human Microbial Associations.Frontiers in genetics · 2019Article
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3 authors.
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Abstract
Studies have shown that microbes exist widely in the human body and are closely related to human complex diseases. Predicting potential associations between microbes and diseases is conducive to understanding the mechanisms of complex diseases and can also facilitate the diagnosis and prevention of human diseases. In this paper, we put forward the Matrix Decomposition and Label Propagation for Human Microbe-Disease Association prediction (MDLPHMDA) on the basis of the dataset of known microbe-disease associations collected from the database of HMDAD and the Gaussian interaction profile kernel similarity for diseases and microbes, disease symptom similarity. Moreover, the performance of our model was evaluated by means of leave-one-out cross validation and five-fold cross validation, and the corresponding AUCs of 0.9034 and 0.8954 ± 0.0030 were gained, respectively. In case studies, 10, 9, 9, and 8 out of the top 10 predicted microbes for asthma, colorectal carcinoma, liver cirrhosis, and type 1 diabetes were confirmed by literatures, respectively. Overall, evaluation results showed that MDLPHMDA has good performance in potential microbe-diseasepositive free parameter, which associations prediction.
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