ArticleInternational journal of biological sciences2018
PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO.
Article in International journal of biological sciences, 2018. 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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Who cites it
17 citing papers in PubMed, 47 citations in OpenAlex.
- 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
- 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
- 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
- SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM.Frontiers in microbiology · 2023Article
- MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.Computational and structural biotechnology journal · 2023Article
- MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.Interdisciplinary sciences, computational life sciences · 2022Article
- Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.Frontiers in microbiology · 2022Article
- Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm.Frontiers in molecular biosciences · 2020Article
- Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection.Frontiers in bioengineering and biotechnology · 2020Article
- WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network.BMC bioinformatics · 2019Article
- Challenges in the construction of knowledge bases for human microbiome-disease associations.Microbiome · 2019Review
- A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations.Frontiers in genetics · 2019Article
- Special issue on Computational Resources and Methods in Biological Sciences.International journal of biological sciences · 2018Article
- Human Microbe-Disease Association Prediction Based on Adaptive Boosting.Frontiers in microbiology · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.
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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.