ArticleOncotarget2017
Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of microbial markers for risk prediction of colorectal neoplasia.British journal of cancer · 2022Pooled it
- Fecal Clostridium symbiosum for Noninvasive Detection of Early and Advanced Colorectal Cancer: Test and Validation Studies.EBioMedicine · 2017Trial
- Coenocline Simulation of Microbiome Samples: A Biologically Mechanistic Framework for Generating Ecologically Realistic Synthetic Datasets to Support Classification Method Evaluation.Pathogens (Basel, Switzerland) · 2026Article
- The Gut Microbiome in Early-Onset Colorectal Cancer: Distinct Signatures, Targeted Prevention and Therapeutic Strategies.Journal of personalized medicine · 2025Review
- Modulating Gut Microbiota with Dietary Components: A Novel Strategy for Cancer-Depression Comorbidity Management.Nutrients · 2025Review
- Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina.Diagnostics (Basel, Switzerland) · 2024Article
- Research progress on the correlation between intestinal flora and colorectal cancer.Frontiers in oncology · 2024Review
- Epidemiology and early screening strategies for colorectal cancer in China.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023Article
- Gut butyrate-producers confer post-infarction cardiac protection.Nature communications · 2023Article
- Progress of gut microbiome and its metabolomics in early screening of colorectal cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Review
- Identification of a novel gut microbiota signature associated with colorectal cancer in Thai population.Scientific reports · 2023Article
- Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.Sensors (Basel, Switzerland) · 2023Article
- Tumor-associated microbiome features of metastatic colorectal cancer and clinical implications.Frontiers in oncology · 2023Article
- Review
- The global research of microbiota in colorectal cancer screening: a bibliometric and visualization analysis.Frontiers in oncology · 2023Article
- Mendelian randomization study of causal link from gut microbiota to colorectal cancer.BMC cancer · 2022Article
- Development and evaluation of a colorectal cancer screening method using machine learning-based gut microbiota analysis.Cancer medicine · 2022Article
- Predictive Modeling of Colonoscopic Findings in a Fecal Immunochemical Test-Based Colorectal Cancer Screening Program.Digestive diseases and sciences · 2022Article
- Microbiome in drug resistance to colon cancer.Current opinion in physiology · 2021Article
- DeepLINK: Deep learning inference using knockoffs with applications to genomics.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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
Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs). As a result, Bayes Net and Random Forest displayed higher accuracies than other algorithms in both populations, although Bayes Net was found with a lower false negative rate than that of Random Forest. Gut microbiota-based prediction was more accurate than the standard fecal occult blood test (FOBT), and the combination of both approaches further improved the prediction accuracy. Moreover, when unclassified OTUs were used as input, the BayesDMNB text algorithm achieved higher accuracy in the Chinese population (AUC=0.994). Taken together, our results suggest that Bayes Net classification model combined with unclassified OTUs may present an accurate method for predicting CRC based on the compositions of gut microbiota.
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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 Socratic graph.