ArticleScience translational medicine2024
Machine learning to detect the SINEs of cancer.
Article in Science translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- Epigenomic modifications define chromatin states to regulate cell-free DNA fragmentomics.Nature communications · 2026Article
- A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.Science advances · 2026Article
- Leveraging cfDNA fragmentomic features for the early detection of colorectal cancer.Frontiers in immunology · 2026Article
- Liquid biopsies across the cancer care continuum.Nature medicine · 2025Review
- Detection of Human Brain Cancers using Genomic and Immune Cell Characterization of Cerebrospinal Fluid through CSF-BAM.Cancer discovery · 2025Article
- Towards adaptive bioelectronic wound therapy with integrated real-time diagnostics and machine learning-driven closed-loop control.npj biomedical innovations · 2025Article
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Fragmentation signatures in cancer patients resemble those of patients with vascular or autoimmune diseases.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cell-free DNA testing for the detection and prognosis prediction of pancreatic cancer.Nature communications · 2025Article
- Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection.Nature reviews. Cancer · 2025Review
- Noninvasive Multicancer Detection Using DNA Hypomethylation of LINE-1 Retrotransposons.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?Biomedicines · 2025Review
- Retrotransposon: an insight into neurological disorders from perspectives of neurodevelopment and aging.Translational neurodegeneration · 2025Review
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
- Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine.Chemical reviews · 2024Review
- The role of biomarkers in the early detection of pancreatic cancer.Familial cancer · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
38 authors at 10 institutions in 3 countries.
Funding
Abstract
We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.