Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach
Fractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural chan...
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MDPI AG
2023-10-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/7/10/771 |
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| author | Igor Pantic Nikola Topalovic Peter R. Corridon Jovana Paunovic |
| author_facet | Igor Pantic Nikola Topalovic Peter R. Corridon Jovana Paunovic |
| author_sort | Igor Pantic |
| collection | DOAJ |
| description | Fractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural changes in cell nuclei during conventional microscopy assessments. In recent years, the fractal dimension and lacunarity of cell nuclei, considered among the most significant FA features, have been suggested as potentially important indicators of cell damage and death. In this study, we demonstrate the feasibility of developing a random forest machine learning model that employs fractal indicators as input data to identify yeast cells treated with oxidopamine (6-hydroxydopamine, 6-OHDA), a powerful toxin commonly applied in neuroscience research. The model achieves notable classification accuracy and discriminatory power, with an area under the receiver operating characteristics curve of more than 0.8. Moreover, it surpasses alternative decision tree models, such as the gradient-boosting classifier, in differentiating treated cells from their intact counterparts. Despite the methodological challenges associated with fractal analysis and random forest training, this approach offers a promising avenue for the continued exploration of machine learning applications in cellular physiology, pathology, and toxicology. |
| first_indexed | 2024-03-10T21:13:36Z |
| format | Article |
| id | doaj.art-43c3d8ffabf5481a954d6e4ea0a93da6 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-10T21:13:36Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-43c3d8ffabf5481a954d6e4ea0a93da62023-11-19T16:35:04ZengMDPI AGFractal and Fractional2504-31102023-10-0171077110.3390/fractalfract7100771Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning ApproachIgor Pantic0Nikola Topalovic1Peter R. Corridon2Jovana Paunovic3Department of Medical Physiology, Faculty of Medicine, University of Belgrade, Višegradska 26/2, RS-11129 Belgrade, SerbiaDepartment of Medical Physiology, Faculty of Medicine, University of Belgrade, Višegradska 26/2, RS-11129 Belgrade, SerbiaDepartment of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab EmiratesDepartment of Pathophysiology, Faculty of Medicine, University of Belgrade, Dr. Subotića 9, RS-11129 Belgrade, SerbiaFractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural changes in cell nuclei during conventional microscopy assessments. In recent years, the fractal dimension and lacunarity of cell nuclei, considered among the most significant FA features, have been suggested as potentially important indicators of cell damage and death. In this study, we demonstrate the feasibility of developing a random forest machine learning model that employs fractal indicators as input data to identify yeast cells treated with oxidopamine (6-hydroxydopamine, 6-OHDA), a powerful toxin commonly applied in neuroscience research. The model achieves notable classification accuracy and discriminatory power, with an area under the receiver operating characteristics curve of more than 0.8. Moreover, it surpasses alternative decision tree models, such as the gradient-boosting classifier, in differentiating treated cells from their intact counterparts. Despite the methodological challenges associated with fractal analysis and random forest training, this approach offers a promising avenue for the continued exploration of machine learning applications in cellular physiology, pathology, and toxicology.https://www.mdpi.com/2504-3110/7/10/771fractal dimensionnucleusartificial intelligencetoxicologycell damage |
| spellingShingle | Igor Pantic Nikola Topalovic Peter R. Corridon Jovana Paunovic Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach Fractal and Fractional fractal dimension nucleus artificial intelligence toxicology cell damage |
| title | Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach |
| title_full | Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach |
| title_fullStr | Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach |
| title_full_unstemmed | Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach |
| title_short | Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach |
| title_sort | oxidopamine induced nuclear alterations quantified using advanced fractal analysis random forest machine learning approach |
| topic | fractal dimension nucleus artificial intelligence toxicology cell damage |
| url | https://www.mdpi.com/2504-3110/7/10/771 |
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