ArticleBMC bioinformatics2019
MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.Bioinformatics (Oxford, England) · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data.BMC microbiology · 2026Article
- Microbiota-host interaction in colorectal cancer: emerging computational technology, multi-omics integration, and mechanisms.Cancer biology & medicine · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- AR-CDT NET: a deep deformable convolutional network for gut microbiome-based disease classification.BMC bioinformatics · 2025Article
- Emerging multifaceted roles of the microbiome in cancer susceptibility.World journal of clinical oncology · 2025Review
- Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Exploring and exploiting the rice phytobiome to tackle climate change challenges.Plant communications · 2024Review
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- AI in microbiome-related healthcare.Microbial biotechnology · 2024Review
- MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.Briefings in bioinformatics · 2024Article
- A permutable MLP-like architecture for disease prediction from gut metagenomic data.BMC bioinformatics · 2024Article
- Deep learning for predicting 16S rRNA gene copy number.Scientific reports · 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
- GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.Bioinformatics (Oxford, England) · 2023Article
- Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2023Article
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2 authors.
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Abstract
backgroundMicrobiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variety of diseases; this suggests that the microbiome profile can be used as a diagnostic tool in identifying the disease states of an individual. However, the high-dimensional nature of metagenomic data poses a significant challenge to existing machine learning models. Consequently, to enable personalized treatments, an efficient framework that can accurately and robustly differentiate between healthy and sick microbiome profiles is needed.
resultsIn this paper, we propose MetaNN (i.e., classification of host phenotypes from Metagenomic data using Neural Networks), a neural network framework which utilizes a new data augmentation technique to mitigate the effects of data over-fitting.
conclusionsWe show that MetaNN outperforms existing state-of-the-art models in terms of classification accuracy for both synthetic and real metagenomic data. These results pave the way towards developing personalized treatments for microbiome related diseases.
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