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DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding siteстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Du Z, Xiao X., Uversky V.N.
- Журнал: Journal of Biomolecular Structure and Dynamics
- Том: 40
- Номер: 9
- Год издания: 2022
- Издательство: Adenine Press
- Местоположение издательства: United States
- Первая страница: 4250
- Последняя страница: 4258
- Аннотация: It’s important to infer the binding site of RNA-binding proteins (RBP) for understanding the interaction between RBP and its RNA targets and decipher the mechanisms of transcriptional regulation. However, experimental detection of RBP binding sites is still time-intensive and expensive. Algorithms based on machine learning can speed up detection of RBP binding sites. In this article, we propose a new deep learning method, DeepA-RBPBS, which can use RNA sequences and structural features to predict RBP binding site. DeepA-RBPBS uses CNN and BiGRU to extract sequences and structural features without long-term dependence issues. It also utilizes an attention mechanism to enhance the contribution of key features. The comparison shows that the performance of DeepA-RBPBS is better than that of the state-of-the-art predictors. In the testing on 31 datasets of CLIP-seq experiments over 19 proteins, MCC (AUC) is 8% (5%) higher than those of the latest method based on deep learning, iDeepS. We also apply DeepA-RBPBS to the target RNA data of RBPs related to diabetes (LIN28, RBFOX2, FTO, IGF2BP2, CELF1 and HuR). The results show that DeepA-RBPBS correctly predicted 41,693 samples, where iDeepS predicted 31,381 samples.Communicated by Ramaswamy H. Sarma.
- Добавил в систему: Уверский Владимир Николаевич