ArticleScientific reports2020
Application of random forest based approaches to surface-enhanced Raman scattering data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed.
- Understanding complex analytical data by a supervised correlation coefficient obtained from random forest.Analytical and bioanalytical chemistry · 2026Review
- Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.Sensors (Basel, Switzerland) · 2026Review
- Endolysosomal Impact of Elevated Ceramide Levels Revealed by Optical and Ultrastructural Nanoprobing.ACS nano · 2026Article
- Reconstruction of Molecular Interaction Patterns from Endolysosomes in Ceramide-Depleted Cells.Nano letters · 2026Article
- Review
- Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection.Discover nano · 2026Article
- Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.Analytical chemistry · 2025Article
- Assessing the acute effect of compression socks on improving arterial compliance in young volunteers.Heliyon · 2025Article
- Raman spectroscopy for agricultural applications.Frontiers in plant science · 2025Article
- Raman Spectroscopy as a Research and Diagnostic Tool in Clinical Hematology and Hematooncology.International journal of molecular sciences · 2024Review
- Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.Molecules (Basel, Switzerland) · 2024Article
- Opening the Random Forest Black Box ofMetabolites · 2023Article
- Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis.The Analyst · 2023Article
- ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.Journal of the American Chemical Society · 2023Article
- Exploitation of surrogate variables in random forests for unbiased analysis of mutual impact and importance of features.Bioinformatics (Oxford, England) · 2023Article
- Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.Journal of Raman spectroscopy : JRS · 2022Article
- Review
- Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.Neural computing & applications · 2022Article
- Machine learning analysis of SERS fingerprinting for the rapid determination ofComputational and structural biotechnology journal · 2022Article
- Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth.Metabolites · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surface-enhanced Raman scattering (SERS) is a valuable analytical technique for the analysis of biological samples. However, due to the nature of SERS it is often challenging to exploit the generated data to obtain the desired information when no reporter or label molecules are used. Here, the suitability of random forest based approaches is evaluated using SERS data generated by a simulation framework that is also presented. More specifically, it is demonstrated that important SERS signals can be identified, the relevance of predefined spectral groups can be evaluated, and the relations of different SERS signals can be analyzed. It is shown that for the selection of important SERS signals Boruta and surrogate minimal depth (SMD) and for the analysis of spectral groups the competing method Learner of Functional Enrichment (LeFE) should be applied. In general, this investigation demonstrates that the combination of random forest approaches and SERS data is very promising for sophisticated analysis of complex biological 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.