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DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.
Briefings in Bioinformatics ( IF 9.5 ) Pub Date : 2020-07-01 , DOI: 10.1093/bib/bbaa124 Quanzhong Liu 1 , Jinxiang Chen 1 , Yanze Wang 1 , Shuqin Li 1 , Cangzhi Jia 2 , Jiangning Song 3 , Fuyi Li 4
Briefings in Bioinformatics ( IF 9.5 ) Pub Date : 2020-07-01 , DOI: 10.1093/bib/bbaa124 Quanzhong Liu 1 , Jinxiang Chen 1 , Yanze Wang 1 , Shuqin Li 1 , Cangzhi Jia 2 , Jiangning Song 3 , Fuyi Li 4
Affiliation
DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.
中文翻译:
DeepTorrent:一种基于深度学习的预测 DNA N4-甲基胞嘧啶位点的方法。
DNA N4-甲基胞嘧啶 (4mC) 是一种重要的表观遗传修饰,在调节 DNA 复制和表达中起着至关重要的作用。然而,通过实验方法检测 4mC 位点是具有挑战性的,这些方法既费时又费钱。因此,可以识别 4mC 位点的计算工具对于理解这种重要类型的 DNA 修饰的机制非常有用。在过去的 3 年中已经提出了几种基于机器学习的 4mC 预测器,尽管它们的性能并不令人满意。深度学习是一种很有前途的技术,可用于开发更准确的 4mC 位点预测。在这项工作中,我们提出了一种基于深度学习的方法,称为 DeepTorrent,用于改进从 DNA 序列中预测 4mC 位点。它结合了四种不同的特征编码方案来编码原始 DNA 序列,并采用多层卷积神经网络与集成双向长短期记忆的初始模块来有效地学习高阶特征表示。然后将来自不同大小过滤器的降维和连接特征图应用于初始模块。此外,还采用了注意力机制和迁移学习技术来训练鲁棒预测器。广泛的基准测试表明,与几种最先进的方法相比,DeepTorrent 显着提高了 4mC 站点预测的性能。
更新日期:2020-07-02
中文翻译:
DeepTorrent:一种基于深度学习的预测 DNA N4-甲基胞嘧啶位点的方法。
DNA N4-甲基胞嘧啶 (4mC) 是一种重要的表观遗传修饰,在调节 DNA 复制和表达中起着至关重要的作用。然而,通过实验方法检测 4mC 位点是具有挑战性的,这些方法既费时又费钱。因此,可以识别 4mC 位点的计算工具对于理解这种重要类型的 DNA 修饰的机制非常有用。在过去的 3 年中已经提出了几种基于机器学习的 4mC 预测器,尽管它们的性能并不令人满意。深度学习是一种很有前途的技术,可用于开发更准确的 4mC 位点预测。在这项工作中,我们提出了一种基于深度学习的方法,称为 DeepTorrent,用于改进从 DNA 序列中预测 4mC 位点。它结合了四种不同的特征编码方案来编码原始 DNA 序列,并采用多层卷积神经网络与集成双向长短期记忆的初始模块来有效地学习高阶特征表示。然后将来自不同大小过滤器的降维和连接特征图应用于初始模块。此外,还采用了注意力机制和迁移学习技术来训练鲁棒预测器。广泛的基准测试表明,与几种最先进的方法相比,DeepTorrent 显着提高了 4mC 站点预测的性能。