使用蛋白质语言模型从蛋白质序列中预测C和S链接的糖化位点
1Department of CSE, BUET, Dhaka 1000, Bangladesh.
Computers in biology and medicine
|March 12, 2025
概括
计算方法可以预测C-链和S-链糖体,这是翻译后的基本修改. 新的模型,DeepCSEmbed-C和DeepCSEmbed-S,提供准确的预测,优于现有方法,并提供可访问的代码.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 后翻译修改后的修改.
背景情况:
- 预测C结合和S结合的葡萄糖体是至关重要的,但在实验上是昂贵的.
- 需要计算方法来有效识别糖酸盐.
- 目前用于甘酸盐预测的方法需要改进.
研究的目的:
- 开发准确的计算模型,用于预测C链和S链糖体.
- 探索语言模型嵌入和顺序特征用于糖酸盐预测.
- 提供可自由访问的数据集和代码,用于糖预测模型.
主要方法:
- 使用递归特征消除 (RFE) 和粒子优化 (PSO) 进行特征选择.
- 采用了三种抽样策略:随机低抽样,SMOTE和ADASYN用于不平衡的数据集.
- 开发了DeepCSEmbed-C (双分支深度学习) 和DeepCSEmbed-S (分类提升) 模型.
主要成果:
- 在独立的数据集上,DeepCSEmbed-C实现了92.9%的灵敏度,95.1%的F1得分和90.6%的MCC.
- 与最先进的方法相比,提出的模型显示出更高的性能.
- 特性选择和采样策略针对模型性能进行了优化.
结论:
- 深CSEmbed-C和深CSEmbed-S是有效的计算工具,用于预测C链和S链糖体.
- 这项研究为研究糖基化研究的研究人员提供了宝贵的资源.
- 开发的模型推进了翻译后修改预测领域.
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