ArticleFrontiers in microbiology2021
Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.Journal of advanced research · 2023Pooled it
- Explainable AI-SERS approach for highly accurate discrimination ofCurrent research in microbial sciences · 2026Article
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Label-free estimation of regulatory T cell activation markers using Raman spectroscopy with machine learning.Scientific reports · 2025Article
- Explainable Deep Learning Framework for SERS Bioquantification.ACS sensors · 2025Article
- Comparison of Raman spectroscopy with mass spectrometry for sequence typing ofMicrobiology spectrum · 2025Article
- Article
- Recent Advances in Surface-Enhanced Raman Scattering for Pathogenic Bacteria Detection: A Review.Sensors (Basel, Switzerland) · 2025Review
- Application of Raman Spectroscopy in Non-Invasive Analysis of the Gut Microbiota and Its Impact on Gastrointestinal Health.Diagnostics (Basel, Switzerland) · 2025Review
- Direct feature identification from Raman spectra and precise data-driven classification of phytopathogens at single conidium-species level.Computational and structural biotechnology journal · 2025Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Surface-Enhanced Raman Spectroscopy Sensor Integrated with Ag@ZIF-8@Au Core-Shell-Shell Nanowire Membrane for Enrichment, Ultrasensitive Detection, and Inactivation of Bacteria in the Environment.ACS applied materials & interfaces · 2024Article
- High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction.Heliyon · 2024Article
- Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.Journal of cellular and molecular medicine · 2024Article
- Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.World journal of microbiology & biotechnology · 2024Article
- Identification ofInfection and drug resistance · 2024Review
- Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.Analytical and bioanalytical chemistry · 2023Review
- Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.Nanoscale advances · 2023Review
- Rapid discrimination ofFrontiers in microbiology · 2023Article
- SERS-Based Biosensors Combined with Machine Learning for Medical Application.ChemistryOpen · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117
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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.