ArticleJournal of chemical information and modeling2024
A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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Who cites it
8 citing papers in PubMed.
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.ACS omega · 2026Article
- Training and validation of a 1D CNN model for accurate three-class classification of oral cancer using serum Raman spectroscopy.Discover oncology · 2026Article
- BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge.Sensors (Basel, Switzerland) · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.bioRxiv : the preprint server for biology · 2026Article
- Review
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis.Chemical reviews · 2025Review
- Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectra are examples of high dimensional data that can often be limited in the number of samples. This is a primary concern when Deep Learning frameworks are developed for tasks such as chemical species identification, quantification, and diagnostics. Open-source data are difficult to obtain and often sparse; furthermore, the collecting and curating of new spectra require expertise and resources. Deep generative modeling utilizes Deep Learning architectures to approximate high dimensional distributions and aims to generate realistic synthetic data. The evaluation of the data and the performance of the deep models is usually conducted on a per-task basis and provides no indication of an increase to robustness, or generalization, on a wider scale. In this study, we compare the benefits and limitations of a standard statistical approach to data synthesis (
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Registered trials
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