Drug-Disease Association Analysis via Machine Learning on Extracted Features by Matrix Decomposition | ||
| Mathematics Interdisciplinary Research | ||
| دوره 10، شماره 3، آذر 2025، صفحه 295-313 اصل مقاله (1.14 M) | ||
| نوع مقاله: Original Scientific Paper | ||
| شناسه دیجیتال (DOI): 10.22052/mir.2025.256471.1506 | ||
| نویسندگان | ||
| Zahra Rafei؛ Seyedeh Fatemeh Hosseini؛ Behnam Yousefimehr؛ Mehdi Ghatee* | ||
| Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, I. R. Iran | ||
| چکیده | ||
| Drug repurposing presents a cost-effective and time-efficient alternative to traditional drug discovery by identifying new therapeutic uses for existing medications. As biomedical data grows in scale and complexity, there is an increasing demand for predictive models that balance accuracy, interpretability, and computational efficiency. In this study, we systematically evaluate hybrid models that combine established matrix factorization techniques with machine learning regressors, with an emphasis on interpretable and lightweight models such as the Decision Tree Regressor. Using the widely adopted Fdataset, comprising 1,933 known associations between 593 drugs and 313 diseases, we demonstrate that several of these hybrid approaches achieve predictive performance comparable to or surpassing that of complex models like WNMFDDA, while significantly reducing memory usage and training time. Notably, our framework relies solely on the drug–disease association matrix, removing the dependency on auxiliary similarity data, which is often unavailable in real-world applications. Among the tested models, the NMF DecisionTreeRegressor offers the highest accuracy, making it ideal for accuracy-critical scenarios, while the Ridge model stands out for its efficiency and suitability for resource-constrained environments. To enhance transparency, we further apply LIME (Local Interpretable Model-Agnostic Explanations) to provide interpretable insights into model predictions. These findings highlight a practical and scalable framework for drug repurposing, particularly suited for environments with limited computational resources. Our approach supports the development of accessible, data-driven predictive tools that accelerate the transition from computational modeling to clinical application. | ||
| کلیدواژهها | ||
| Drug-disease association؛ Machine learning؛ Matrix decomposition؛ Hybrid models | ||
| مراجع | ||
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