ArticleJournal of translational medicine2021
MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- A microbe-drug association prediction model based on graph attention networks and rotation forest.Frontiers in bioinformatics · 2026Article
- A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships.BMC medical informatics and decision making · 2025Article
- Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery.Frontiers in pharmacology · 2025Article
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations.Microorganisms · 2024Review
- Microbe-disease associations prediction by graph regularized non-negative matrix factorization withJournal of cellular and molecular medicine · 2024Article
- ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations.Journal of cellular and molecular medicine · 2024Article
- Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.BMC biology · 2024Article
- A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.BMC bioinformatics · 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
- Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance.BMC biology · 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
- GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder.BMC bioinformatics · 2022Article
- Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.Frontiers in microbiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrobes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments.
methodsIn this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities.
resultsThe AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature.
conclusionsAll the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.
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.