ArticleFrontiers in microbiology2019
A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network.
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 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges.Sensors (Basel, Switzerland) · 2022Pooled it
- Development and validation of embedded multilayer attention graph convolution neural network models for predicting gut microbe-disease associations.Frontiers in microbiology · 2026Article
- 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
- A novel bi-directional heterogeneous network selection method for disease and microbial association prediction.BMC bioinformatics · 2022Article
- MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.Interdisciplinary sciences, computational life sciences · 2022Article
- Generating weighted and thresholded gene coexpression networks using signed distance correlation.Network science (Cambridge University Press) · 2022Article
- Robust gene coexpression networks using signed distance correlation.Bioinformatics (Oxford, England) · 2021Article
- Editorial: Predictive Modeling of Human Microbiota and Their Role in Health and Disease.Frontiers in microbiology · 2021Article
- Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network.Frontiers in microbiology · 2020Article
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance.
Indexed as
Identifiers
What OpenQuestion holds
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.