Ge, C.; Hu, X.; Zhang, L.; Li, H.

Transcriptome sequencing (RNA-seq) is widely used in cancer research to study the transcriptome and its role in disease progression. Somatic copy number aberrations (SCNAs) are key drivers of cancer development, and inferring SCNAs from RNA-seq data can provide critical insights for disease classification and treatment prediction. We introduce RCANE, a deep learning-based method designed to predict genome-wide SCNAs across various cancer types using RNA-seq data. RCANE is trained on data from The Cancer Genome Atlas (TCGA) and DepMap cancer cell lines, demonstrating superior performance compared to existing methods. This scalable approach offers a robust solution for improving SCNA prediction in cancer diagnostics and treatment.