ArticleFrontiers in microbiology2020
RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization.
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 29 citations in OpenAlex.
- Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.Scientific reports · 2025Article
- Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery.Frontiers in pharmacology · 2025Article
- Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.BMC biology · 2024Article
- Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion.Frontiers in microbiology · 2024Article
- Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance.BMC biology · 2023Article
- Leveraging pre-trained language models for mining microbiome-disease relationships.BMC bioinformatics · 2023Article
- Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.Frontiers in microbiology · 2023Article
- SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM.Frontiers in microbiology · 2023Article
- Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization.Frontiers in genetics · 2022Article
- Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data.Frontiers in genetics · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
- NNAN: Nearest Neighbor Attention Network to Predict Drug-Microbe Associations.Frontiers in microbiology · 2022Article
- Mining microbe-disease interactions from literature via a transfer learning model.BMC bioinformatics · 2021Article
- Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction.Frontiers in microbiology · 2021Article
- A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins.Frontiers in genetics · 2021Review
- Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study.Frontiers in microbiology · 2021Article
- Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization.Frontiers in genetics · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.
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