ArticleCommunications biology2025
RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.
Article in Communications biology, 2025. 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.
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.Communications biology · 2025Article
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
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Authors and funding
4 authors.
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Abstract
Transcriptome sequencing (RNA-seq) of cancers is widely employed in cancer research to investigate gene expression patterns and their role in disease progression. Somatic copy-number aberrations (SCNAs)-critical genomic drivers of tumorigenesis-can also be inferred directly from RNA-seq, yielding a "two-for-one" return of quantitative expression measures plus structural-variation calls at a fraction of the cost of separate DNA assays. Here, we present RCANE, a deep-learning framework that predicts genome-wide SCNAs across diverse cancer types using only RNA-seq data. Trained on The Cancer Genome Atlas (TCGA) and DepMap cell-line cohorts, RCANE consistently outperforms existing approaches, delivering a scalable, robust solution for improving somatic copy-number aberration profiling in cancer diagnostics and therapeutic decision-making.
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Registered trials
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