卷积神经网络
计算机科学
人工智能
特征(语言学)
集成学习
环状RNA
编码(内存)
编码(社会科学)
机器学习
深度学习
特征提取
人工神经网络
核糖核酸
模式识别(心理学)
计算生物学
生物
数学
基因
哲学
语言学
生物化学
统计
作者
Dilan Lasantha,S. M. Vidanagamachchi,Sam Nallaperuma
标识
DOI:10.1016/j.compbiomed.2024.108466
摘要
Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.
科研通智能强力驱动
Strongly Powered by AbleSci AI