ArticleInternational journal of molecular sciences2023
Using Machine Learning Methods to Study Colorectal Cancer Tumor Micro-Environment and Its Biomarkers.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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.
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Who cites it
32 citing papers in PubMed, 38 citations in OpenAlex.
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- Screening, Prognostic, and Predictive Molecular Tools for Colorectal Cancer: Recent Advances in the Classical Background.International journal of molecular sciences · 2026Review
- Integrative network pharmacology and machine learning identify potential targets of indole-3-lactic acid in colorectal cancer.PloS one · 2026Article
- Bioinformatics combined with machine learning for the identification of malignant transformation markers in colorectal polyps.Frontiers in molecular biosciences · 2026Article
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- Prognostic value of neuro-related genes in colorectal cancer and their potential implications for immunotherapy.Journal of Cancer · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Engineering Osteosarcoma In Vitro: From Traditional Models to Biofabricated Platforms for Precision Medicine.ACS omega · 2025Review
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
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- A prognostic model for gastric cancer constructed by multiple machine learning algorithms.Journal of molecular histology · 2025Article
- Colorectal Cancer Detection Through Sweat Volatilome Using an Electronic Nose System and GC-MS Analysis.Cancers · 2025Article
- A novel neutrophil extracellular trap-related gene signature for predicting glioblastoma prognosis.Scientific reports · 2025Article
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- Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.Scientific reports · 2025Article
- Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study.World journal of surgical oncology · 2025Article
- Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide, and the identification of biomarkers can improve early detection and personalized treatment. In this study, RNA-seq data and gene chip data from TCGA and GEO were used to explore potential biomarkers for CRC. The SMOTE method was used to address class imbalance, and four feature selection algorithms (MCFS, Borota, mRMR, and LightGBM) were used to select genes from the gene expression matrix. Four machine learning algorithms (SVM, XGBoost, RF, and kNN) were then employed to obtain the optimal number of genes for model construction. Through interpretable machine learning (IML), co-predictive networks were generated to identify rules and uncover underlying relationships among the selected genes. Survival analysis revealed that
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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 OpenQuestion graph.