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6mA-Pred:基于深度学习识别DNA N6-甲基腺嘌呤位点

6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning.

作者信息

Huang Qianfei, Zhou Wenyang, Guo Fei, Xu Lei, Zhang Lichao

机构信息

College of Intelligence and Computing, Tianjin University, Tianjin, China.

School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.

出版信息

PeerJ. 2021 Feb 3;9:e10813. doi: 10.7717/peerj.10813. eCollection 2021.

Abstract

With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, , and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.

摘要

随着关于6mA修饰位点的数据积累,越来越多的学者开始关注6mA位点的识别。尽管6mA位点的重要性已得到认可,但仍缺乏识别它们的方法,现有的大多数方法旨在在单个物种中识别它们。在本研究中,我们旨在开发一种适用于多个物种的识别方法。基于先前的研究,我们提出了一种6mA位点识别方法。我们的实验证明,所提出的6mA-Pred方法对于识别来自水稻、……和人类等分类群基因中的6mA位点是有效的。一系列实验结果表明,6mA-Pred是一种优秀的方法。我们提供了该研究中使用的源代码,可从http://39.100.246.211:5004/6mA_Pred/获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/835f/7866889/53719b93df86/peerj-09-10813-g001.jpg

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