ArticleNPJ precision oncology2024
Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Towards liquid biopsy-based analysis of antitumour immunity.Nature reviews. Clinical oncology · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- MethylSense: high accuracy machine learning-based diagnostics forJournal of clinical microbiology · 2026Article
- A comprehensive toolkit for analyzing cell-free DNA genomic sequencing data in liquid biopsy.iScience · 2026Article
- fRagmentomics: an R package for integrating cell-free DNA fragment features with mutational status to support liquid biopsy interpretation.Bioinformatics (Oxford, England) · 2026Article
- DeepFRAG: a method for cancer detection based on DNA fragmentomics and deep learning.Bioinformatics advances · 2026Article
- Improved circulating tumor DNA profiling by simultaneous extraction of DNA methylation and copy number information from methylated DNA sequencing data.NPJ precision oncology · 2025Article
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Circulating Cell-free DNA Fragmentomics Detection and Beyond.Aging and disease · 2025Review
- Homologous recombination repair gene alteration is strongly associated with more prostate-specific membrane antigen-positive metastases in newly diagnosed hormone-sensitive prostate cancer with ≤5 conventional imaging defined distant metastases.European journal of nuclear medicine and molecular imaging · 2025Article
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs. We include 4606 samples subjected to different types of cfDNA sequencing to develop EIMIT, and subsequently evaluate classification performance of linear projections of EMIT on six datasets and an additional inhouse testing set encopassing whole-genome, whole-genome bisulfite and 5-hydroxymethylcytosine sequencing. The linear projection of representations from EMIT achieved area under the receiver operating curve (AUROC) values ranged from 0.895 (0.835-0.955) to 0.996 (0.994-0.997) across these six datasets, outperforming its baseline by significant margins. Additionally, we showed that linear projection of EMIT representations can achieve an AUROC of 0.962 (0.914-1.0) in identification of lung cancer on an independent testing set subjected to whole-exome sequencing. The findings of this study indicate that a transformer-based deep learning model can learn cancer-discrimative representations from cfDNA end-motifs. The representations of this deep learning model can be exploited for discriminating patients with and without cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.