DcDiRNeSa, Drug Combination Prediction by Integrating Dimension Reduction and Negative Sampling Techniques | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 3، مهر 2023، صفحه 417-427 اصل مقاله (1.86 M) | ||
| نوع مقاله: Applied Article | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.12883.2437 | ||
| نویسندگان | ||
| Mina Tabatabaei1؛ Hossein Rahmani* 2؛ Motahareh Nasiri1 | ||
| 1School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
| 2School of Computer engineering, Iran University of Science and Technology, Tehran, Iran. | ||
| چکیده | ||
| The search for effective treatments for complex diseases, while minimizing toxicity and side effects, has become crucial. However, identifying synergistic combinations of drugs is often a time-consuming and expensive process, relying on trial and error due to the vast search space involved. Addressing this issue, we present a deep learning framework in this study. Our framework utilizes a diverse set of features, including chemical structure, biomedical literature embedding, and biological network interaction data, to predict potential synergistic combinations. Additionally, we employ autoencoders and principal component analysis (PCA) for dimension reduction in sparse data. Through 10-fold cross-validation, we achieved an impressive 98 percent area under the curve (AUC), surpassing the performance of seven previous state-of-the-art approaches by an average of 8%. | ||
| کلیدواژهها | ||
| Drug Combination Prediction؛ Synergistic effect؛ Computational techniques؛ Deep Learning | ||
| مراجع | ||
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