ArticleGenome medicine2022
Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The application of AI-driven and engineered intratumoral microbes in cancer therapy.Journal of translational medicine · 2025Review
- Tracing microbial hazards in the aquatic supply chain: challenges, technologies, and future directions.Frontiers in nutrition · 2025Review
- Deep learning for predicting 16S rRNA gene copy number.Scientific reports · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 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
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- EXPERT: transfer learning-enabled context-aware microbial community classification.Briefings in bioinformatics · 2022Article
- Microbial Dark Matter: from Discovery to Applications.Genomics, proteomics & bioinformatics · 2022Review
- Ontology-aware neural network: a general framework for pattern mining from microbiome data.Briefings in bioinformatics · 2022Article
- Microbiome Sample Comparison and Search: From Pair-Wise Calculations to Model-Based Matching.Frontiers in microbiology · 2021Review
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Authors and funding
8 authors.
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Abstract
The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST .
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