ArticleSynthetic and systems biotechnology2022
Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data.
Article in Synthetic and systems biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 63 citations in OpenAlex.
- Microbiome and cancer: mechanistic insights, diagnostic potential, and therapeutic strategies.Frontiers in cell and developmental biology · 2026Review
- Identification of a weighted urinary microbial signature for bladder cancer discrimination.Frontiers in oncology · 2026Article
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota.BMC genomics · 2024Article
- CAIM: coverage-based analysis for identification of microbiome.Briefings in bioinformatics · 2024Article
- Interpretable machine learning decodes soil microbiome's response to drought stress.Environmental microbiome · 2024Article
- CAIM: Coverage-based Analysis for Identification of Microbiome.bioRxiv : the preprint server for biology · 2024Article
- Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance.PLoS computational biology · 2024Article
- Mitochondrial genome plasticity of mammalian species.BMC genomics · 2024Article
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.PLoS computational biology · 2023Article
- Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy.Journal of translational medicine · 2023Article
- Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.GigaScience · 2022Article
- Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer.Frontiers in cellular and infection microbiology · 2022Article
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3 authors at 1 institution in 1 country.
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Abstract
Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles. Based on within data cross-validation and cross-dataset prediction, we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken. We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial organisms to further increase the prediction performance for colorectal cancer from metagenomes.
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