Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis
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 typ...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/10/9/181 |
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| author | Christopher T. Mandrell Torrey E. Holland James F. Wheeler Sakineh M. A. Esmaeili Kshitij Amar Farhan Chowdhury Poopalasingam Sivakumar |
| author_facet | Christopher T. Mandrell Torrey E. Holland James F. Wheeler Sakineh M. A. Esmaeili Kshitij Amar Farhan Chowdhury Poopalasingam Sivakumar |
| author_sort | Christopher T. Mandrell |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:32:31Z |
| format | Article |
| id | doaj.art-17b657747903416796dd751df531b1a2 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-10T16:32:31Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-17b657747903416796dd751df531b1a22023-11-20T12:43:25ZengMDPI AGLife2075-17292020-09-0110918110.3390/life10090181Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature AnalysisChristopher T. Mandrell0Torrey E. Holland1James F. Wheeler2Sakineh M. A. Esmaeili3Kshitij Amar4Farhan Chowdhury5Poopalasingam Sivakumar6Department of Physics, Southern Illinois University Carbondale, Neckers 483-A, 1245 Lincoln Drive, Carbondale, IL 62901, USADepartment of Physics, Southern Illinois University Carbondale, Neckers 483-A, 1245 Lincoln Drive, Carbondale, IL 62901, USADepartment of Physics, Southern Illinois University Carbondale, Neckers 483-A, 1245 Lincoln Drive, Carbondale, IL 62901, USADepartment of Mechanical Engineering and Energy Processes, Southern Illinois University Carbondale, 1263 Lincoln Drive, Carbondale, IL 62901, USADepartment of Mechanical Engineering and Energy Processes, Southern Illinois University Carbondale, 1263 Lincoln Drive, Carbondale, IL 62901, USADepartment of Mechanical Engineering and Energy Processes, Southern Illinois University Carbondale, 1263 Lincoln Drive, Carbondale, IL 62901, USADepartment of Physics, Southern Illinois University Carbondale, Neckers 483-A, 1245 Lincoln Drive, Carbondale, IL 62901, USAA 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.https://www.mdpi.com/2075-1729/10/9/181tumor repopulating cell (TRC)support vector machine (SVM)k-nearest neighbor (kNN)principal component analysis (PCA)Raman spectroscopypancreatic cancer |
| spellingShingle | Christopher T. Mandrell Torrey E. Holland James F. Wheeler Sakineh M. A. Esmaeili Kshitij Amar Farhan Chowdhury Poopalasingam Sivakumar Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis Life tumor repopulating cell (TRC) support vector machine (SVM) k-nearest neighbor (kNN) principal component analysis (PCA) Raman spectroscopy pancreatic cancer |
| title | Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis |
| title_full | Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis |
| title_fullStr | Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis |
| title_full_unstemmed | Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis |
| title_short | Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis |
| title_sort | machine learning approach to raman spectrum analysis of mia paca 2 pancreatic cancer tumor repopulating cells for classification and feature analysis |
| topic | tumor repopulating cell (TRC) support vector machine (SVM) k-nearest neighbor (kNN) principal component analysis (PCA) Raman spectroscopy pancreatic cancer |
| url | https://www.mdpi.com/2075-1729/10/9/181 |
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