ArticleFrontiers in molecular biosciences2022
Improve the Colorectal Cancer Diagnosis Using Gut Microbiome Data.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hierarchical Multi-Omics Trajectory Prediction for fecal microbiota transplantation: a novel machine learning framework for small-sample longitudinal multi-omics integration.Briefings in bioinformatics · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- GGut microbes · 2025Review
- Emerging multifaceted roles of the microbiome in cancer susceptibility.World journal of clinical oncology · 2025Review
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the United States, colorectal cancer is the second largest cause of cancer death, and accurate early detection and identification of high-risk patients is a high priority. Although fecal screening tests are available, the close relationship between colorectal cancer and the gut microbiome has generated considerable interest. We describe a machine learning method for gut microbiome data to assist in diagnosing colorectal cancer. Our methodology integrates feature engineering, mediation analysis, statistical modeling, and network analysis into a novel unified pipeline. Simulation results illustrate the value of the method in comparison to existing methods. For predicting colorectal cancer in two real datasets, this pipeline showed an 8.7% higher prediction accuracy and 13% higher area under the receiver operator characteristic curve than other published work. Additionally, the approach highlights important colorectal cancer-related taxa for prioritization, such as high levels of
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