Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification
The global burden of Type II diabetes demands innovative strategies that combine predictive tools with targeted therapies. This study applies machine learning to the PIMA Indian dataset, identifying glucose, BMI, and age as key predictors, and integrates these with biological pathway mapping to sup...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138766 |
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| author | Iram Wajahat Fazel Keshtkar Syed Ahmad Chan Bukhari |
| author_facet | Iram Wajahat Fazel Keshtkar Syed Ahmad Chan Bukhari |
| author_sort | Iram Wajahat |
| collection | DOAJ |
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The global burden of Type II diabetes demands innovative strategies that combine predictive tools with targeted therapies. This study applies machine learning to the PIMA Indian dataset, identifying glucose, BMI, and age as key predictors, and integrates these with biological pathway mapping to support precision medicine. A novel methodological contribution is pathway mapping without gene-level data, linking clinical features to diabetes-related mechanisms like insulin signaling and PPAR pathways. This approach is particularly valuable for population datasets lacking molecular detail. In addition to established therapies (e.g., PPAR-based treatments, AMPK activators), the study explores emerging options such as dual GLP-1/GIP receptor agonists, novel AMPK activators, and sirtuin-related targets. Phytochemicals with multi-target effects are also evaluated. By bridging predictive modeling and biological insight, this research presents a framework for early detection and therapeutic innovation in T2DM, with special relevance for genetically predisposed populations such as the PIMA Indians.
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| format | Article |
| id | doaj-art-bdfc6b0baf2a4f7084b6d1c70875c91f |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-bdfc6b0baf2a4f7084b6d1c70875c91f2025-08-20T02:30:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138766Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target IdentificationIram Wajahat0Fazel Keshtkar1Syed Ahmad Chan Bukhari2 St. John’s University, NY St. John’s University, NYSt. John's University The global burden of Type II diabetes demands innovative strategies that combine predictive tools with targeted therapies. This study applies machine learning to the PIMA Indian dataset, identifying glucose, BMI, and age as key predictors, and integrates these with biological pathway mapping to support precision medicine. A novel methodological contribution is pathway mapping without gene-level data, linking clinical features to diabetes-related mechanisms like insulin signaling and PPAR pathways. This approach is particularly valuable for population datasets lacking molecular detail. In addition to established therapies (e.g., PPAR-based treatments, AMPK activators), the study explores emerging options such as dual GLP-1/GIP receptor agonists, novel AMPK activators, and sirtuin-related targets. Phytochemicals with multi-target effects are also evaluated. By bridging predictive modeling and biological insight, this research presents a framework for early detection and therapeutic innovation in T2DM, with special relevance for genetically predisposed populations such as the PIMA Indians. https://journals.flvc.org/FLAIRS/article/view/138766 |
| spellingShingle | Iram Wajahat Fazel Keshtkar Syed Ahmad Chan Bukhari Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification |
| title_full | Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification |
| title_fullStr | Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification |
| title_full_unstemmed | Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification |
| title_short | Integrating Machine Learning and Pathway Analysis for Precision Medicine in Type 2 Diabetes: Predictive Modeling and Therapeutic Target Identification |
| title_sort | integrating machine learning and pathway analysis for precision medicine in type 2 diabetes predictive modeling and therapeutic target identification |
| url | https://journals.flvc.org/FLAIRS/article/view/138766 |
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