对偶(语法数字)
计算机科学
生物信息学
鉴定(生物学)
机制(生物学)
计算生物学
非编码RNA
编码(社会科学)
长非编码RNA
人工智能
核糖核酸
生物
遗传学
基因
数学
认识论
艺术
哲学
文学类
统计
植物
作者
Tianyuan Liu,Bohao Zou,Manman He,Yongfei Hu,Yiying Dou,Tianyu Cui,Puwen Tan,Shaobin Li,Shuan Rao,Yan Huang,Sixi Liu,Kaican Cai,Dong Wang
摘要
Abstract Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as ‘dual functional lncRNAs’. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.
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