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Updated: May 16, 2025

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[基于蛋白质语言模型的突变效应预测研究进展]
Liang Zhang1, Pan Tan2,3, Liang Hong1,2,3,4
1School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
概括
蛋白质语言模型 (PLM) 通过改进对蛋白质突变效应的预测来彻底改变生物信息学. 本综述涵盖了序列,结构和组合模型,讨论了挑战和未来的方向.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 预测蛋白质突变效应对于生物信息学和蛋白质工程至关重要.
- 深度学习,特别是蛋白质语言模型 (PLM),提供了新的方法.
研究的目的:
- 审查PLM在预测蛋白质突变效应中的应用.
- 分析不同的PLM类型,它们的原理和局限性.
主要方法:
- 专注于基于序列的,基于结构的和混合的PLM.
- 在PLM培训中讨论无监督和监督学习.
- 分析数据质量和数据噪声等挑战.
主要成果:
- 在预测蛋白质突变效应方面,PLM显著有前途.
- 不同的模型架构提供不同的优势和局限性.
- 当前的挑战包括数据采集和噪音处理.
结论:
- PLM是预测蛋白质突变效应的强大工具.
- 未来的研究应该探索多式联络融合和少量学习.
- 本综述为推进该领域提供了一个全面的视角.
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