Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures
Abstract Computational oncology advances multi-targeted therapies for Colon Cancer (CC) by leveraging molecular data and identifying potential drug candidates. However, challenges persist in understanding CC molecular pathways and identifying essential genes. This research integrates biomarker signa...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-01058-6 |
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| author | Tingting Liu Lifan Zhong Xizhe Sun Zhijiang He Witiao Lv Liyun Deng Yanfei Chen |
| author_facet | Tingting Liu Lifan Zhong Xizhe Sun Zhijiang He Witiao Lv Liyun Deng Yanfei Chen |
| author_sort | Tingting Liu |
| collection | DOAJ |
| description | Abstract Computational oncology advances multi-targeted therapies for Colon Cancer (CC) by leveraging molecular data and identifying potential drug candidates. However, challenges persist in understanding CC molecular pathways and identifying essential genes. This research integrates biomarker signatures from high-dimensional gene expression, mutation data, and protein interaction networks. The research study employs Adaptive Bacterial Foraging (ABF) optimization to refine search parameters, maximizing the predictive accuracy of therapeutic outcomes. The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. The ABF-CatBoost integration facilitates a multi-targeted therapeutic approach, addressing drug resistance by analyzing mutation patterns, adaptive resistance mechanisms, and conserved binding sites. External validation datasets assess predictive accuracy and generalizability. The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). The model predicts toxicity risks, metabolism pathways, and drug efficacy profiles, ensuring safer and more effective treatments. The artificial intelligence model personalizes therapy by leveraging patient-specific molecular profiles, optimizing drug selection and dosage while minimizing side effects. By altering the biomarker selection and pathway analysis components, this computational framework is modified for other cancers, expanding its application and impact in personalized cancer treatment. It also improves precision medicine in CC therapy, speeding up drug discovery and improving therapeutic outcomes. |
| format | Article |
| id | doaj-art-bfcf222629f74c40ae374bec4642f6df |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-bfcf222629f74c40ae374bec4642f6df2025-08-24T11:05:52ZengNature Portfolionpj Precision Oncology2397-768X2025-08-019111310.1038/s41698-025-01058-6Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signaturesTingting Liu0Lifan Zhong1Xizhe Sun2Zhijiang He3Witiao Lv4Liyun Deng5Yanfei Chen6Hainan Pharmaceutical Research and Development Science and Technology Park, Hainan Medical University, HaikouHainan Pharmaceutical Research and Development Science and Technology Park, Hainan Medical University, HaikouHainan Pharmaceutical Research and Development Science and Technology Park, Hainan Medical University, HaikouDepartment of Orthopedics, Hainan Provincial Corps Hospital of Chinese People’s Armed Police Force, HaikouSchool of Pharmacy, Hainan Medical University, HaikouSchool of Pharmacy, Hainan Medical University, HaikouHainan Pharmaceutical Research and Development Science and Technology Park, Hainan Medical University, HaikouAbstract Computational oncology advances multi-targeted therapies for Colon Cancer (CC) by leveraging molecular data and identifying potential drug candidates. However, challenges persist in understanding CC molecular pathways and identifying essential genes. This research integrates biomarker signatures from high-dimensional gene expression, mutation data, and protein interaction networks. The research study employs Adaptive Bacterial Foraging (ABF) optimization to refine search parameters, maximizing the predictive accuracy of therapeutic outcomes. The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. The ABF-CatBoost integration facilitates a multi-targeted therapeutic approach, addressing drug resistance by analyzing mutation patterns, adaptive resistance mechanisms, and conserved binding sites. External validation datasets assess predictive accuracy and generalizability. The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). The model predicts toxicity risks, metabolism pathways, and drug efficacy profiles, ensuring safer and more effective treatments. The artificial intelligence model personalizes therapy by leveraging patient-specific molecular profiles, optimizing drug selection and dosage while minimizing side effects. By altering the biomarker selection and pathway analysis components, this computational framework is modified for other cancers, expanding its application and impact in personalized cancer treatment. It also improves precision medicine in CC therapy, speeding up drug discovery and improving therapeutic outcomes.https://doi.org/10.1038/s41698-025-01058-6 |
| spellingShingle | Tingting Liu Lifan Zhong Xizhe Sun Zhijiang He Witiao Lv Liyun Deng Yanfei Chen Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures npj Precision Oncology |
| title | Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures |
| title_full | Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures |
| title_fullStr | Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures |
| title_full_unstemmed | Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures |
| title_short | Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures |
| title_sort | machine learning driven multi targeted drug discovery in colon cancer using biomarker signatures |
| url | https://doi.org/10.1038/s41698-025-01058-6 |
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