ArticleScientific reports2022
Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI-empowered human microbiome research.Gut · 2026Review
- Comparing the performance of functional versus taxonomic metagenomics for detecting ammonia disturbances in the biogas system.FEMS microbiology ecology · 2026Article
- Harnessing the Microbiome in Cancer Immunotherapy: Regulation, Prediction, and Therapeutic Targeting.Annual review of immunology · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Virulome over taxonomy: refining the driver-passenger model in colorectal carcinogenesis.Frontiers in cellular and infection microbiology · 2026Review
- Gut microbiota and their influence in brain cancer milieu.Journal of neuroinflammation · 2025Review
- Gut microbiome in colorectal cancer: metagenomics from bench to bedside.JNCI cancer spectrum · 2025Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.Frontiers in microbiology · 2024Review
- Microbiomes, Their Function, and Cancer: How Metatranscriptomics Can Close the Knowledge Gap.International journal of molecular sciences · 2023Review
- Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model.Frontiers in molecular biosciences · 2023Article
- Progress in the diagnosis of lymph node metastasis in rectal cancer: a review.Frontiers in oncology · 2023Review
- Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics.International journal of molecular sciences · 2022Review
- Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes.Cancers · 2022Article
- Antitumor effects of fecal microbiota transplantation: Implications for microbiome modulation in cancer treatment.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.
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