ReviewLab on a chip2018
Machine learning to detect signatures of disease in liquid biopsies - a user's guide.
Review in Lab on a chip, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 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
67 citing papers in PubMed.
- Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Cell-free DNA in sepsis: from molecular insights to clinical management.Military Medical Research · 2025Review
- Liquid biopsy - a narrative review with an update on current US governmental clinical trials targeting immunotherapy.Future science OA · 2025Review
- From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.Diagnostics (Basel, Switzerland) · 2025Review
- Emerging biomarkers for early cancer detection and diagnosis: challenges, innovations, and clinical perspectives.European journal of medical research · 2025Review
- miRNA panel from HER2+ and CD24+ plasma extracellular vesicle subpopulations as biomarkers of early-stage breast cancer.Breast cancer research : BCR · 2025Article
- Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.Molecular neurobiology · 2025Review
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- Immunomodulatory role of vitamin D and emerging immunotherapies in hepatocellular carcinoma.Frontiers in nutrition · 2025Review
- Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review.Current cancer drug targets · 2025Review
- Recent progress in prompt molecular detection of liquid biopsy using Cas enzymes: innovative approaches for cancer diagnosis and analysis.Journal of translational medicine · 2024Review
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
- Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine.Chemical reviews · 2024Review
- Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.Briefings in bioinformatics · 2024Article
- Review
- Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.Therapeutic advances in vaccines and immunotherapy · 2024Review
- Progress and application of lung-on-a-chip for lung cancer.Frontiers in bioengineering and biotechnology · 2024Review
- Quantifying the potential benefits of early detection for pancreatic cancer through a counterfactual simulation modeling analysis.Scientific reports · 2023Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.
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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.