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植物Nh-Kcr:一种深度学习模型,用于预测植物中的非海斯顿化位点

Yanming Jiang¹, Renxiang Yan^2,3, Xiaofeng Wang⁴

¹College of Mathematics and Computer Sciences, Shanxi Normal University, Taiyuan, 030031, China.

|February 15, 2024

概括

我们开发了PlantNh-Kcr,这是一个深度学习模型,用于预测植物中的lysine crotonylation (Kcr) 位点. 这种工具准确地识别了植物非海斯顿Kcr位点,克服了实验的局限性,帮助了植物生物学研究.

科学领域:

植物分子生物学植物分子生物学
蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.

背景情况:

氨酸化 (Kcr) 是一种重要的翻译后修饰,调节植物生物过程.
实验性Kcr站点检测是昂贵和低效的,需要计算方法.
现有的Kcr遗址预测模型主要关注人类,为植物特定工具留下了一个空白.

研究的目的:

开发一种计算模型,用于预测植物非海斯顿氨酸化基位.
为了解决植物特定Kcr预测工具和数据集的稀缺性.
为植物化研究提供一个宝贵的资源.

主要方法:

从五种植物物种收集和分析了非基因组Kcr位点.
开发了一个深度学习模型,PlantNh-Kcr,集成CNN,BiLSTM和注意力机制.
使用五倍交叉验证和独立测试评估模型性能.

主要成果:

与传统和其他深度学习模型相比,PlantNh-Kcr表现出优异的性能.
分析显示,在多种植物物种上训练的一般模型的表现优于特定物种的模型.
大多数已识别的植物Kcr位点位于非基因组蛋白上.

结论:

植物Nh-Kcr是一种有效的工具,用于预测植物非基因素Kcr位点.
开发的模型可以显著推进植物化研究.
多种培训方法提高了植物Kcr地点的预测准确性.

关键词:

注意力机制注意力机制双向的长期短期记忆.卷积神经网络是一种卷积神经网络.克罗托尼化是什么意思焦点损失焦点损失

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