A novel machine learning approach for tumor detection based on telomeric signatures.

ABSTRACT: Cancer remains one of the most complex diseases faced by humanity, with over 200 distinct types, each characterized by unique molecular profiles that demand specialized therapeutic approaches. [Tomczak et al. (Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Współczesna Onkol 2015;1A:68-77.)] Prior studies have shown that both short and long telomere lengths (TLs) are associated with elevated cancer risk, underscoring the intricate relationship between TL variation and tumorigenesis. [Haycock et al. (Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study. JAMA Oncol 2017;3:636-51.)] To investigate this relationship, we developed a supervised machine learning model trai

SUBMITTER: Shah P 

PROVIDER: S-EPMC12627404 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature