Predicting Anticancer Drug Repurposing Candidates using Knowledge Graphs
- سال انتشار: 1403
- محل انتشار: چهارمین همایش بین المللی و سیزدهمین همایش ملی بیوانفورماتیک
- کد COI اختصاصی: IBIS13_132
- زبان مقاله: انگلیسی
- تعداد مشاهده: 56
نویسندگان
School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Engineering Science, College of Engineering, University of Tehran, Iran
Centers of Excellence for Pharmaceutical Processes, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
چکیده
Drug repurposing (DR) offers a promising and efficient alternative to traditional drug discovery by identifying new therapeutic applications for existing drugs, reducing the time and costs associated with development. This study introduces a novel framework that leverages a new hybrid knowledge graph integrating drug, disease, and protein interactions, combined with a dual-channel Convolutional Neural Network for drug-disease association prediction. The knowledge graph captures complex biological relationships through diverse biomedical data, while the neural network architecture enhances the model's ability to extract meaningful patterns. The framework demonstrates superior performance, achieving an AUC of ۰.۹۸۳۶ and AUPRC of ۰.۹۶۸۶, significantly outperforming state-of-the-art methods. To enhance the reliability of these predictions, molecular docking simulations were conducted, providing crucial biological validation. Integrating advanced machine learning with robust biological validation offers a promising avenue for accelerating drug discovery efforts and addressing critical unmet medical needs.کلیدواژه ها
Drug repurposing, Dual_Channel Neural network, Knowledge Graph, Anticancerاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.