ReviewGenome research2025
Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.
Review in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches.BMC pregnancy and childbirth · 2026Pooled it
- Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- Circulating Cell-free DNA as a biomarker for radiation-induced injury: From mechanistic insights to clinical translation.Cancer metastasis reviews · 2026Review
- Multifeature sequencing-based liquid biopsy for cancer diagnosis and monitoring.Genome medicine · 2026Review
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.Cancers · 2026Review
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- Translational Perspectives on Cell-Free Mitochondrial DNA as a Biomarker in Gynecological Cancers: Current Limitations and Future Research Directions.Biomolecules · 2026Review
- Machine learning and artificial intelligence in liquid biopsy-based early detection of pancreatic cancer: a scoping review.BJC reports · 2026Review
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- Circulating RNA as a Functional Component of Liquid Biopsy in Cancer: Concepts, Classification, and Clinical Applications.International journal of molecular sciences · 2026Review
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
- Review
- Liquid biopsy epigenetics: establishing a molecular profile based on cell-free DNA.Molecular oncology · 2026Review
- Artificial intelligence-powered signal analysis of loop-mediated isothermal amplification (LAMP) for the screening of Kaposi sarcoma at the point of care.Sensors & diagnostics · 2026Article
- Long-read sequencing for cancer liquid biopsy: advancing precision oncology.Frontiers in oncology · 2026Review
- Cell-free DNA in sepsis: from molecular insights to clinical management.Military Medical Research · 2025Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Unlocking the Potential of Liquid Biopsy: A Paradigm Shift in Endometrial Cancer Care.Diagnostics (Basel, Switzerland) · 2025Review
- Application of Digital Polymerase Chain Reaction (dPCR) in Non-Invasive Prenatal Testing (NIPT).Biomolecules · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.
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Identifiers
What Socratic 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 Socratic graph.