ArticleNature biomedical engineering2025
A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends and hotspots of circulating tumor DNA in colorectal cancer: a bibliometric study.Frontiers in oncology · 2025Pooled it
- Advancing the applications of liquid biopsies in oncology.Nature reviews. Genetics · 2026Review
- Toward generalizable prediction of cancer signal using a cell-free DNA language model.Cell reports. Medicine · 2026Article
- Off-Target-Based Tumor Fraction Estimation from Targeted Sequencing Shows Concordance with Orthogonal Methods Across Advanced Solid Tumors.International journal of molecular sciences · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Article
- Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics.Cancers · 2026Review
- Dynamic Precision Oncology for Real-Time Molecular Monitoring and Management in Urothelial Carcinoma.International journal of molecular sciences · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Review
- Machine learning-based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma.Communications medicine · 2026Article
- Liquid biopsies for detection, characterization, and interception of therapy resistance in thoracic malignancies.Frontiers in oncology · 2026Review
- SNAP: Streamlined Nextflow Analysis Pipeline for Immunoprecipitation-Based Epigenomic Profiling of Circulating Chromatin.bioRxiv : the preprint server for biology · 2025Article
- Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types.Nature communications · 2025Article
- GCfix: a fast and accurate fragment length-specific method for correcting GC bias in cell-free DNA.Bioinformatics (Oxford, England) · 2025Article
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
32 authors.
Funding
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Abstract
The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named 'Fragle', by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method's versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility.
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