ArticleScientific reports2018
Novel human microbe-disease associations inference based on network consistency projection.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- In Search of Healthy Ageing: A Microbiome-Based Precision Nutrition Approach for Type 2 Diabetes Prevention.Nutrients · 2025Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 2025Article
- MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.Computational and structural biotechnology journal · 2023Article
- Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.Frontiers in microbiology · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
- Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method.Frontiers in physiology · 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
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing evidence shows that microbes are closely related to various human diseases. Obtaining a comprehensive and detailed understanding of the relationships between microbes and diseases would not only be beneficial to disease prevention, diagnosis and prognosis, but also would lead to the discovery of new drugs. However, because of a lack of data, little effort has been made to predict novel microbe-disease associations. To date, few methods have been proposed to solve the problem. In this study, we developed a new computational model based on network consistency projection to infer novel human microbe-disease associations (NCPHMDA) by integrating Gaussian interaction profile kernel similarity of microbes and diseases, and symptom-based disease similarity. NCPHMDA is a non-parametric and global network based model that combines microbe space projection and disease space projection to achieve the final prediction. Experimental results demonstrated that the integrated space projection of microbes and diseases, and symptom-based disease similarity played roles in the model performance. Cross validation frameworks and case studies further illustrated the superior predictive performance over other methods.
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Registered trials
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.