ArticleFrontiers in microbiology2019
A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.
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 24 papers.
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Who cites it
24 citing papers in PubMed.
- Gut microbiota dysbiosis and the gut-lung axis in COPD: mechanisms, clinical relevance, and microbiota-targeted interventions.Frontiers in cell and developmental biology · 2026Review
- Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction.Journal of pharmaceutical analysis · 2025Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 2025Article
- Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning.Frontiers in genetics · 2025Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Computational prediction of new therapeutic effects of probiotics.Scientific reports · 2024Article
- Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion.Frontiers in microbiology · 2024Article
- A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.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
- MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.Computational and structural biotechnology journal · 2023Article
- 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
- Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.Frontiers in microbiology · 2023Article
- Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy.Frontiers in neuroscience · 2022Article
- Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci.Frontiers in oncology · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
- RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.Molecular genetics and genomics : MGG · 2021Review
- 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
- Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network.Frontiers in microbiology · 2021Article
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Authors and funding
6 authors.
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Abstract
A growing number of clinical observations have indicated that microbes are involved in a variety of important human diseases. It is obvious that in-depth investigation of correlations between microbes and diseases will benefit the prevention, early diagnosis, and prognosis of diseases greatly. Hence, in this paper, based on known microbe-disease associations, a prediction model called NBLPIHMDA was proposed to infer potential microbe-disease associations. Specifically, two kinds of networks including the disease similarity network and the microbe similarity network were first constructed based on the Gaussian interaction profile kernel similarity. The bidirectional label propagation was then applied on these two kinds of networks to predict potential microbe-disease associations. We applied NBLPIHMDA on Human Microbe-Disease Association database (HMDAD), and compared it with 3 other recent published methods including LRLSHMDA, BiRWMP, and KATZHMDA based on the leave-one-out cross validation and 5-fold cross validation, respectively. As a result, the area under the receiver operating characteristic curves (AUCs) achieved by NBLPIHMDA were 0.8777 and 0.8958 ± 0.0027, respectively, outperforming the compared methods. In addition, in case studies of asthma, colorectal carcinoma, and Chronic obstructive pulmonary disease, simulation results illustrated that there are 10, 10, and 8 out of the top 10 predicted microbes having been confirmed by published documentary evidences, which further demonstrated that NBLPIHMDA is promising in predicting novel associations between diseases and microbes as well.
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