人工智能
卷积神经网络
N6-甲基腺苷
计算机科学
特征(语言学)
相关性(法律)
深度学习
核糖核酸
支持向量机
机器学习
计算生物学
基质(水族馆)
甲基化
模式识别(心理学)
生物
基因
生物化学
法学
甲基转移酶
哲学
语言学
生态学
政治学
作者
Yuxuan Wu,Yuxin Zhang,Ruoqi Wang,Jia Meng,Kunqi Chen,Yiyou Song,Daiyun Huang
标识
DOI:10.1145/3469678.3469706
摘要
N6-methyladenosine (m6A), one of the most common post-transcriptional mRNA modifications, has been proved to correlate with multiple biological functions through the process of binding to specific m6A reader proteins. Various m6A readers exist among the genome of human beings, however, owing to the scarce wet experiments related to this topic, the binding specificity of proteins was not elucidated. Therefore, a deep learning approach combined with CNN and RNN frameworks was generated to predict the epitranscriptome-wide targets of six m6A reader proteins (YTHDF1-3, YTHDC1-2, EIF3A). Additionally, layer-wise relevance calculation was conducted to obtain each input feature contribution and tried to explain the model training process. Finally, we achieved superior performance in the classification, with an average AUROC of 0.942 in EIF3A full transcript, higher than the typical conventional machine learning algorithms (SVM) under the same condition. Moreover, we quantified the most optimal sequence length (1001bp) during the m6A reader substrate prediction. This research paves the way for further RNA methylation target prediction and functional characterization of m6A readers.
科研通智能强力驱动
Strongly Powered by AbleSci AI