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A novel machine learning approach for tumor detection based on telomeric signatures
Priyanshi Shah1, Arun Sethuraman1
1Department of Biology, San Diego State University, 5500 Campanile Dr, San Diego CA 92182, United States.
Biology Methods & Protocols
|November 21, 2025
Summary
This study uses machine learning to predict cancer status by analyzing telomere length and genomic data. The model achieved 82.62% accuracy, offering a new tool for cancer diagnostics and risk assessment.
Area of Science:
- Genomics
- Oncology
- Bioinformatics
Background:
- Cancer is a complex disease with over 200 types, requiring tailored therapies.
- Telomere length (TL) variation is linked to cancer risk, indicating its role in tumorigenesis.
Purpose of the Study:
- To develop a predictive model for tumor status using telomere biology and genomic data.
- To investigate the potential of telomere length as a cancer biomarker.
Main Methods:
- Developed a supervised machine learning model.
- Trained the model on telomeric read content, genomic variants, and phenotypic features.
- Utilized data from 33 cancer types in The Cancer Genome Atlas (TCGA).
Main Results:
- The model achieved 82.62% accuracy in predicting tumor status.
- The trained model is publicly available on GitHub for further development.
Conclusions:
- Telomere length variation can serve as a predictive biomarker in oncology.
- This multidisciplinary approach integrates telomere biology with large-scale genomic and phenotypic data for improved cancer diagnostics and risk assessment.
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