ArticleLife (Basel, Switzerland)2020
Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis.
Article in Life (Basel, Switzerland), 2020. 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 trial behind it
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Who cites it
8 citing papers in PubMed.
- Integrating spatial and chemical information enhances differentiation of non-alcoholic steatohepatitis states in Raman imaging.Scientific reports · 2025Article
- Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma.Scientific reports · 2025Article
- Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.Biochemical and biophysical research communications · 2023Article
- Confocal Raman Micro-Spectroscopy for Discrimination of Glycerol Diffusivity in Ex Vivo PorcineLife (Basel, Switzerland) · 2022Article
- Machine Learning Approach for Application-Tailored Nanolubricants' Design.Nanomaterials (Basel, Switzerland) · 2022Article
- Recent Advances in Surface-Enhanced Raman Scattering Magnetic Plasmonic Particles for Bioapplications.Nanomaterials (Basel, Switzerland) · 2021Review
- Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection ofFrontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.
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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.