ArticleJournal of Raman spectroscopy : JRS2022
Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.
Article in Journal of Raman spectroscopy : JRS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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.
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Who cites it
27 citing papers in PubMed, 70 citations in OpenAlex.
- Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.Sensors (Basel, Switzerland) · 2026Review
- Emerging Plasmonic Nanomaterials for SERS-Based Disease Diagnostics: Innovations, Clinical Challenges, and AI Integration.Molecules (Basel, Switzerland) · 2026Review
- Exosome-Based Liquid Biopsy in Biliary Tract Cancer: Nanotechnology-Enabled Strategies and Future Perspectives.International journal of nanomedicine · 2026Review
- Surface enhanced Raman spectroscopy and machine learning as an accurate and rapid diagnostic tool for hydrocephalus.Scientific reports · 2025Article
- Research on developing SERS substrates based on a ZnO porous membrane decorated with silver nanoparticles aimed at detecting low concentrations of Rhodamine B.RSC advances · 2025Article
- SERS-guided photodynamic therapy: pioneering strategies in advanced cancer diagnosis and treatment.Discover nano · 2025Review
- Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore.The journal of physical chemistry letters · 2025Article
- Studying the Synthesis of Silver Nanocubes and Their Structural Evolution under Controlled Galvanic Reactions.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Direct SERS Detection of Nucleic Acids in the Presence of Spermine: A Unified Nanoparticle Platform Allows for the Elucidation of Surface Adsorption Hierarchies.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model.Nano letters · 2025Article
- DeepATsers: a deep learning framework for one-pot SERS biosensor to detect SARS-CoV-2 virus.Scientific reports · 2025Article
- Surface Enhanced Raman Scattering for Biomolecular Sensing in Human Healthcare Monitoring.ACS nano · 2025Review
- Trends and Advances in Wearable Plasmonic Sensors Utilizing Surface-Enhanced Raman Spectroscopy (SERS): A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.BMC medicine · 2025Article
- Multianalyte nano-biosensor diagnostics: advances through microfluidic and AI integration.Frontiers in bioengineering and biotechnology · 2025Review
- Smart CAR-T Nanosymbionts: archetypes and proto-models.Frontiers in immunology · 2025Review
- Nanoplasmonics biosensors: At the frontiers of biomedical diagnostics.Trends in analytical chemistry : TRAC · 2024Article
- Paper/GO/e-Au flexible SERS sensors forNanoscale advances · 2024Article
- RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components.ACS omega · 2024Article
- Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE
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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.