ArticleRSC advances2019
Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips.
Article in RSC advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.The veterinary quarterly · 2026Article
- Multiplexed Detection of Cancer Protein Biomarkers via Surface-Enhanced Raman Scattering Immunoassays on Natural Diatomite Strips.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Review: Detection of Cancer Biomarkers from a Clinical Perspective.International journal of molecular sciences · 2025Review
- Artificial Intelligent-Enhanced Metabolite Profiling for Intraoperative IDH1 Genotyping in Glioma Using an Orthogonally Responsive SERS Probe.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A new methodology for sub-femtomolar detection of organic molecules through the combination of surface-enhanced Raman spectroscopy and a superhydrophobic fluidic concentrator.Analytical science advances · 2025Article
- Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.Environmental science & technology · 2024Review
- RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components.ACS omega · 2024Article
- High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction.Heliyon · 2024Article
- Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.Biosensors · 2023Review
- Recent advances of Au@Ag core-shell SERS-based biosensors.Exploration (Beijing, China) · 2023Review
- Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.Journal of Raman spectroscopy : JRS · 2022Article
- Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset.Bioengineering (Basel, Switzerland) · 2022Article
- Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency.Applied spectroscopy · 2022Article
- Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Specificity is a challenge in liquid biopsy and early diagnosis of various diseases. There are only a few biomarkers that have been approved for use in cancer diagnostics; however, these biomarkers suffer from a lack of high specificity. Moreover, determining the exact type of disorder for patients with positive liquid biopsy tests is difficult, especially when the aberrant expression of one single biomarker can be found in various other disorders. In this study, a SERS-based protein biomarker detection platform in a microfluidic chip and two machine learning algorithms (K-nearest neighbor and classification tree) are used to improve the reproducibility and specificity of the SERS-based liquid biopsy assay. Applying machine learning algorithms to the analysis of the expression level data of 5 protein biomarkers (CA19-9, HE4, MUC4, MMP7, and mesothelin) in pancreatic cancer patients, ovarian cancer patients, pancreatitis patients, and healthy individuals improves the chance of recognition for one specific disorder among the aforementioned diseases with overlapping protein biomarker changes. Our results demonstrate a convenient but highly specific approach for cancer diagnostics using serum samples.
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.