دورية أكاديمية

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder

التفاصيل البيبلوغرافية
العنوان: KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder
المؤلفون: Xin-Fei Wang, Chang-Qing Yu, Zhu-Hong You, Yan Qiao, Zheng-Wei Li, Wen-Zhun Huang, Ji-Ren Zhou, Hai-Yan Jin
المصدر: iScience, Vol 26, Iss 8, Pp 107478- (2023)
بيانات النشر: Elsevier, 2023.
سنة النشر: 2023
المجموعة: LCC:Science
مصطلحات موضوعية: Gene network, Neural networks, Science
الوصف: Summary: Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the ‘behavior relationships’ of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2589-0042
Relation: http://www.sciencedirect.com/science/article/pii/S2589004223015559; https://doaj.org/toc/2589-0042
DOI: 10.1016/j.isci.2023.107478
URL الوصول: https://doaj.org/article/5bc8fd9ee3e84ca1a8b68b982a45a382
رقم الأكسشن: edsdoj.5bc8fd9ee3e84ca1a8b68b982a45a382
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:25890042
DOI:10.1016/j.isci.2023.107478