ArticleInternational journal of clinical oncology2024
A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.
Article in International journal of clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Biologically-informed regional subset analysis with CatBoost for robust tissue-of-origin prediction.PloS one · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundGenome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites.
methodsAnalyzing methylation data from 890 samples across 10 cancer types from the TCGA database, we utilized ANOVA and Gain Ratio to select the most significant CpG sites, then employed Gradient Boosting to reduce these to just 100 sites.
resultsThis approach maintained high accuracy across multiple machine learning models, with classification accuracy rates between 87.7% and 93.5% for methods including Extreme Gradient Boosting, CatBoost, and Random Forest. This method effectively minimizes the number of features needed without losing performance, helping to classify primary organs and uncover subgroups within specific cancers like breast and lung.
conclusionsUsing a gradient boosting feature selector shows potential for streamlining methylation-based cancer classification.
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