ArticleACS omega2022
Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- In vivo Raman spectroscopy for real-time biochemical assessment of tissue pathology and physiology.Nature protocols · 2026Review
- Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.Advances in experimental medicine and biology · 2026Review
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Development of a rapid and novel diagnostic technique for cardiac amyloidosis using Raman spectroscopy.Research square · 2025Article
- 3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.Analytical chemistry · 2025Article
- Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches.NPJ science of food · 2025Review
- Variational autoencoder enhanced analysis of energy metabolism and autophagy in exercising cardiomyocytes.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging.Biomedical optics express · 2024Article
- A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data.Journal of chemical information and modeling · 2024Article
- Article
- Article
- Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution.Nature communications · 2023Article
- First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics.ACS applied materials & interfaces · 2023Article
- The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine.Chemical reviews · 2023Review
- Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms.Frontiers in oncology · 2023Article
- Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications.Biosensors · 2022Review
- A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.Biosensors · 2022Article
- Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.Biosensors · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.
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.