ArticleGenome biology2025
CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks.Bioinformatics advances · 2026Article
- SoMaCX: a complex generative genome modeling framework.BMC genomics · 2025Article
- araCNA: somatic copy number profiling using long-range sequence models.NAR genomics and bioinformatics · 2025Article
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- Single-cell copy number calling and event history reconstruction.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
2 authors.
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Abstract
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.
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Registered trials
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