Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models
Abstract This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nano...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84450-9 |
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| author | Wael A. Mahdi Adel Alhowyan Ahmad J. Obaidullah |
| author_facet | Wael A. Mahdi Adel Alhowyan Ahmad J. Obaidullah |
| author_sort | Wael A. Mahdi |
| collection | DOAJ |
| description | Abstract This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN). The selection of these models was based on their wide range of capabilities in dealing with non-linear relationships and data complexity. To further model performance and strength, the study also applied cutting-edge methods including the Firefly Algorithm for hyperparameter tuning and Recursive Feature Elimination (RFE) for feature selection. Based on higher R² and lower RMSE values for most output parameters, the study concluded that Kernel Ridge Regression (KRR) did better compared to other models in predicting biodistribution outcomes. The study revealed that machine learning models, particularly KRR, exhibit a high level of efficiency in accurately representing the non-linear characteristics of nanoparticle biodistribution. The results obtained provide valuable insights into the optimization of predictive models for the behavior of nanoparticles. These models can be further enhanced by the use of advanced feature selection and hyperparameter tuning techniques. |
| format | Article |
| id | doaj-art-100ff4fcefdb442b8d9311951935c550 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-100ff4fcefdb442b8d9311951935c5502025-01-12T12:21:42ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-84450-9Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning modelsWael A. Mahdi0Adel Alhowyan1Ahmad J. Obaidullah2Department of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud UniversityAbstract This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN). The selection of these models was based on their wide range of capabilities in dealing with non-linear relationships and data complexity. To further model performance and strength, the study also applied cutting-edge methods including the Firefly Algorithm for hyperparameter tuning and Recursive Feature Elimination (RFE) for feature selection. Based on higher R² and lower RMSE values for most output parameters, the study concluded that Kernel Ridge Regression (KRR) did better compared to other models in predicting biodistribution outcomes. The study revealed that machine learning models, particularly KRR, exhibit a high level of efficiency in accurately representing the non-linear characteristics of nanoparticle biodistribution. The results obtained provide valuable insights into the optimization of predictive models for the behavior of nanoparticles. These models can be further enhanced by the use of advanced feature selection and hyperparameter tuning techniques.https://doi.org/10.1038/s41598-024-84450-9Cancer treatmentDrug nanoparticlesKernel Ridge regressionBayesian ridge regressionK-nearest neighbors |
| spellingShingle | Wael A. Mahdi Adel Alhowyan Ahmad J. Obaidullah Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models Scientific Reports Cancer treatment Drug nanoparticles Kernel Ridge regression Bayesian ridge regression K-nearest neighbors |
| title | Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| title_full | Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| title_fullStr | Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| title_full_unstemmed | Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| title_short | Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| title_sort | intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models |
| topic | Cancer treatment Drug nanoparticles Kernel Ridge regression Bayesian ridge regression K-nearest neighbors |
| url | https://doi.org/10.1038/s41598-024-84450-9 |
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