ArticleMethods (San Diego, Calif.)2019
MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.
Article in Methods (San Diego, Calif.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- AI-empowered human microbiome research.Gut · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- kMermaid: Ultrafast metagenomic read assignment to protein clusters by hashing of amino acid k-mer frequencies.PLoS computational biology · 2025Article
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- phylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.Bioinformatics (Oxford, England) · 2024Article
- Model-free prediction of microbiome compositions.Microbiome · 2024Article
- Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs.bioRxiv : the preprint server for biology · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.Bioinformatics (Oxford, England) · 2023Article
- Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Scientific novelty beyond the experiment.Microbial biotechnology · 2023Article
- Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.Scientific reports · 2023Article
- POPDx: an automated framework for patient phenotyping across 392 246 individuals in the UK Biobank study.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.
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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.