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• W3184231723 endingPage "1155" @default.
• W3184231723 startingPage "1155" @default.
• W3184231723 abstract "RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N6-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction of m6A sites is crucial because it can provide a better understanding of their functional mechanisms. In this regard, although experimental methods are useful, they are time consuming. Previously, researchers have attempted to predict m6A sites using computational methods to overcome the limitations of experimental methods. Some of these approaches are based on classical machine-learning techniques that rely on handcrafted features and require domain knowledge, whereas other methods are based on deep learning. However, both methods lack robustness and yield low accuracy. Hence, we develop a branch-based convolutional neural network and a novel RNA sequence representation. The proposed network automatically extracts features from each branch of the designated inputs. Subsequently, these features are concatenated in the feature space to predict the m6A sites. Finally, we conduct experiments using four different species. The proposed approach outperforms existing state-of-the-art methods, achieving accuracies of 94.91%, 94.28%, 88.46%, and 94.8% for the H. sapiens, M. musculus, S. cerevisiae, and A. thaliana datasets, respectively." @default.
• W3184231723 created "2021-08-02" @default.
• W3184231723 creator A5027996912 @default.
• W3184231723 creator A5054148817 @default.
• W3184231723 date "2021-07-28" @default.
• W3184231723 modified "2023-10-06" @default.
• W3184231723 title "bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network" @default.
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• W3184231723 doi "https://doi.org/10.3390/genes12081155" @default.
• W3184231723 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8392086" @default.
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• W3184231723 hasPublicationYear "2021" @default.
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