ReviewLife (Basel, Switzerland)2021
Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey.
Review in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Tracing the history of clinical practice of liquid biopsy: a bibliometric analysis.Frontiers in immunology · 2025Pooled it
- Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review.International journal of molecular sciences · 2023Pooled it
- A pre-trained large generative model for translating single-cell transcriptomes to proteomes.Nature biomedical engineering · 2026Article
- Microfluidic approaches for liquid biopsy in glioblastoma: Insights into diagnostic and follow-up strategies.Bioengineering & translational medicine · 2026Review
- Early multi-cancer detection using liquid biopsy: emerging biomarkers and clinical strategies.Frontiers in oncology · 2026Review
- Salivary biomarkers in oral cancer diagnosis: advancing conventional treatment strategies.Military Medical Research · 2026Review
- Advancing precision medicine in hepatocellular carcinoma: current challenges and future directions in liquid biopsy, immune microenvironment, single nucleotide polymorphisms, and conversion therapy.Hepatic oncology · 2025Review
- Non-invasive Diagnostics in the Focus: The Importance of Salivary Biomarkers in Detecting Oral Cancer.Cureus · 2025Review
- From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.Diagnostics (Basel, Switzerland) · 2025Review
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Unveiling the impact of corticosteroid therapy on liquid biopsy-detected cell-free DNA levels in meningioma and glioblastoma patients.The journal of liquid biopsy · 2024Article
- Article
- Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection.Discover oncology · 2024Article
- Liquid biopsy into the clinics: Current evidence and future perspectives.The journal of liquid biopsy · 2024Review
- The Circulating Biomarkers League: Combining miRNAs with Cell-Free DNAs and Proteins.International journal of molecular sciences · 2024Review
- The global patent landscape of artificial intelligence applications for cancer.Nature biotechnology · 2023Article
- Liquid biopsies for cancer: From bench to clinic.MedComm · 2023Review
- Review
- A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction.Basic research in cardiology · 2023Review
- Integrative analysis of multi-omics data for liquid biopsy.British journal of cancer · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.
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.