ArticleCancers2021
Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Identification of exosomal miRNA-based predictive signatures for gestational diabetes mellitus via multi-algorithm machine learning.BMC pregnancy and childbirth · 2026Article
- Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach.JMIR formative research · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Article
- Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.RNA biology · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma.Clinical epigenetics · 2025Article
- cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling.Briefings in bioinformatics · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours.Epigenomics · 2025Article
- Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation.NPJ precision oncology · 2025Article
- Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.Frontiers in bioinformatics · 2025Article
- A cost-effective method for combining the power of genetic and epigenetic selection in animal production.Environmental epigenetics · 2025Article
- Gut Microbiota as Mediator and Moderator Between Hepatitis B Virus and Hepatocellular Carcinoma: A Prospective Study.Cancer medicine · 2024Article
- PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.Briefings in bioinformatics · 2024Article
- Stanniocalcin Protein Expression in Female Reproductive Organs: Literature Review and Public Cancer Database Analysis.Endocrinology · 2024Review
- Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.Cancer discovery · 2024Article
- Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.Biology methods & protocols · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.
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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.