ReviewNPJ precision oncology2024
A review of machine learning methods for cancer characterization from microbiome data.
Review in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence at the gut-oral microbiota frontier: mapping machine learning tools for gastric cancer risk prediction.Biomedical engineering online · 2025Pooled it
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package.Nature protocols · 2026Review
- Programming the tumor microenvironment through microbiome-driven mechanisms.Frontiers in cellular and infection microbiology · 2026Review
- Microbiome and cancer: mechanistic insights, diagnostic potential, and therapeutic strategies.Frontiers in cell and developmental biology · 2026Review
- The microbiome as a systems-level regulator of immune, metabolic, neural, and endocrine signaling in cancer.Frontiers in immunology · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025Article
- Advancements in Cancer Survival Prediction: A Systematic Review of Classical and Modern Approaches.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Review
- Harnessing gut microbiota for colorectal cancer therapy: from clinical insights to therapeutic innovations.NPJ biofilms and microbiomes · 2025Review
- Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Gut microbiota and their influence in brain cancer milieu.Journal of neuroinflammation · 2025Review
- Advances in gut microbiota-related treatment strategies for managing colorectal cancer in humans.Cancer biology & medicine · 2025Review
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms.Animal microbiome · 2025Article
- Machine learning models reveal Saccharomyces yeasts are associated with poor piglet growth in early development.Journal of animal science · 2025Article
- From laboratory to clinic: opportunities and challenges of functional food active ingredients in cancer therapy.Frontiers in nutrition · 2025Review
- The cancer microbiome.Advances in clinical chemistry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.
Identifiers
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.