ArticleiScience2022
Human disease prediction from microbiome data by multiple feature fusion and deep learning.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Gut Microbiome Signatures in Multiple Sclerosis: A Case-Control Study with Machine Learning and Global Data Integration.Biomedicines · 2025Article
- MSFT-transformer: a multistage fusion tabular transformer for disease prediction using metagenomic data.Briefings in bioinformatics · 2025Article
- Diffusion model for imputing time-series gut microbiome profiles using phylogenetic information and metadata integration.Bioinformatics advances · 2025Article
- MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.Briefings in bioinformatics · 2024Article
- scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.iScience · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.iScience · 2023Article
- Article
- MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data.Briefings in bioinformatics · 2023Article
- Machine learning for data integration in human gut microbiome.Microbial cell factories · 2022Review
- Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.
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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.