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使用混合神经网络架构用于DNA序列表示:对N⁴-甲基细胞因位点的研究

Van-Nui Nguyen¹, Trang-Thi Ho², Thu-Dung Doan³

¹University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Viet Nam.

Computers in biology and medicine

|June 14, 2024

概括

这项研究使用先进的深度学习增强了Fragaria vesca中的N4-甲基细胞素 (4mC) 位点预测. 最好的模型,一个CNN与fastText,显著提高了对罗萨属植物物种的表观遗传研究的准确性.

科学领域:

基因组学和表观遗传学
生物信息学和计算生物学
植物科学植物科学

背景情况:

N4-甲基细胞氨酸 (4mC) 是一种关键的DNA修饰,参与了各种植物基因组的表观遗传调节.
罗萨ceae家族,包括重要的水果作物,表现出4mC的修饰,对基因表达,适应和进化有影响.
准确预测4mC地点对于了解其在植物发育和应激反应中的功能作用至关重要.

研究的目的:

开发一种高精度的计算模型,用于预测Fragaria vesca基因组中的4mC位点.
通过集成先进的功能编码和深度学习架构来改进现有的预测方法.
为罗萨属植物物种的表观遗传研究提供一个强大的工具.

主要方法:

利用深度学习,包括卷积神经网络 (CNN),循环神经网络 (RNN) 和长期短期记忆 (LSTM) 网络.
集成了先进的功能编码技术和预训练的自然语言处理 (NLP) 模型,如fastText.
在独立数据集上使用灵敏度,特异性和准确度评估模型性能.

主要成果:

性能最好的模型是使用fastText编码的CNN架构.
该模型实现了高预测性能,灵敏度为0.909,特异性为0.77,准确度为0.879.
开发的模型表现出优越的预测能力,相比之前发布的方法在同一数据集.

结论:

增强的深度学习模型显著提高了Fragaria vesca.的N4-甲基细胞素 (4mC) 部位预测的准确性.
这一进步为罗莎属植物的表观遗传学研究提供了宝贵的工具,有助于对基因调节和适应的研究.
这些发现突显了将NLP技术与深度学习相结合的潜力,以精确地识别植物基因组中的表观遗传标记.

关键词:

DNA N(4) - 甲基细胞氨酸深度学习是一种深度学习.自然语言处理自然语言处理.罗莎属植物 (Rosaceae) 的基因组序列分析是指进行序列分析.字体嵌入字体嵌入.

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