开发预测工具的进展,用于检测导致蛋白质突变的疾病
M Michael Gromiha1, Medha Pandey1, A Kulandaisamy1
1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Computers in biology and medicine
|December 5, 2024
概括
本综述探讨了用于预测引起疾病的蛋白质突变的计算方法. 它涵盖数据库,功能和先进的工具,如机器学习和大型语言模型,以了解突变的影响.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 蛋白质突变改变结构,稳定性,结合性和功能,可能导致疾病.
- 了解这些突变效应对于了解疾病机制和治疗策略至关重要.
- 现有多种方法可以识别致病突变,推动了该领域的研究.
研究的目的:
- 审查用于识别引起疾病的蛋白质突变的计算预测方法的发展.
- 为突变预测提供现有数据库和基于序列/结构的特征的概述.
- 讨论机器学习,深度学习和大型语言模型在这个领域的应用.
主要方法:
- 数据库编译致病突变的数据库.
- 分析基于序列和结构的特征,用于预测建模.
- 讨论计算工具,包括机器学习,深度学习和大型语言模型.
- 专注于特定的疾病领域 (癌症,神经退行性,传染性) 和蛋白质类型 (膜蛋白).
主要成果:
- 目前数据库和预测特征的概述.
- 突出机器学习,深度学习和用于突变效应预测的大型语言模型的进步.
- 重点是预测关键疾病点和膜蛋白的突变.
- 识别用于突变效应分析的计算资源.
结论:
- 计算方法正在进步,用于预测引起疾病的突变.
- 需要进一步改进,以提高现有的预测工具的准确性和范围.
- 本综述为理解和利用突变研究中的计算方法提供了一个资源.
关键词:
算法算法是一种算法.结合性亲缘关系是一种结合性亲缘关系.癌症热点是癌症的热点.数据库 数据库就是数据库.引起疾病的突变.传染性疾病 传染性疾病机器学习就是机器学习.膜蛋白是一种膜蛋白.网络 网络 网络 网络 网络 网络这是神经退行性疾病.基于序列的特征是基于序列的特征.稳定的稳定性 稳定的稳定性结构 结构 是一个结构.更多相关视频
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