ResLysEmbed:一个基于ResNet的框架,用于使用序列和语言模型嵌入式来预测 succinylated lysine残留物
Souvik Ghosh1,2, Md Muhaiminul Islam Nafi1,3, M Saifur Rahman1
1Department of CSE, BUET, Dhaka 1000, Bangladesh.
Bioinformatics advances
|September 8, 2025
概括
我们开发了ResLysEmbed,这是一个新的深度学习模型,用于预测氨酸化位点. 这种方法通过结合蛋白质语言模型和ResNet架构来提高准确性,帮助疾病研究.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 氨酸糖化是一种重要的翻译后修饰,影响细胞功能和疾病发展.
- 现有的计算工具难以准确预测化部位,阻碍了研究进展.
研究的目的:
- 开发一种先进的计算模型,精确预测氨酸化部位.
- 为此预测任务确定最佳的蛋白质语言模型和深度学习架构.
主要方法:
- 提出了ResLysEmbed,这是一个基于ResNet的新型架构,集成了来自蛋白质语言模型的词和每余分嵌入.
- 对比了各种蛋白质语言模型和深度学习架构,包括像ConvLysEmbed和InceptLysEmbed这样的混合模型.
- 使用Shapley添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- ResLysEmbed表现出卓越的性能,在独立的测试集中获得了高精度,MCC和F1分.
- 该模型的性能优于现有的化部位预测方法.
- SHAP分析提供了关于残留物贡献和对预测准确性的位置影响的见解.
结论:
- ResLysEmbed在电脑预测 lysine succinylation 中取得了重大进展.
- 该模型的可解释性增强了对化机制的理解.
- 开发的工具和代码是公开的,以促进进一步的研究.
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