ArticleScientific reports2023
A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.Frontiers in genetics · 2026Article
- HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations.Interdisciplinary sciences, computational life sciences · 2025Article
- KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.Medical & biological engineering & computing · 2025Article
- Computational analysis of the gut microbiota-mediated drug metabolism.Computational and structural biotechnology journal · 2025Article
- Article
- GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.Scientific reports · 2024Article
- Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.Bioinformatics (Oxford, England) · 2024Article
- A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.BMC bioinformatics · 2024Article
- LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble.Frontiers in microbiology · 2024Article
- BANNMDA: a computational model for predicting potential microbe-drug associations based on bilinear attention networks and nuclear norm minimization.Frontiers in microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.
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.