整合性机器学习预测了用于精确瘤学的激酶突变的激活.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
基诺姆-AI是一种新的机器学习工具,可以预测基因突变激酶是否会激活它们. 这有助于通过识别针对性治疗的关键突变来个性化癌症治疗.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 基因组学和遗传学 基因组学和遗传学
背景情况:
- 蛋白激酶是调节细胞过程的重要酶;突变的异常激活驱动癌症的进展.
- 了解酶误解突变对于个性化癌症治疗和预测药物疗效至关重要.
- 目前的方法很难准确预测酶突变的功能影响.
研究的目的:
- 开发一个精确的机器学习框架,Kinome-AI,用于将激酶误传突变分类为激活或非激活.
- 整合多模式数据,包括序列和结构特征,以提高预测能力.
- 为了能够精确地识别致癌的激酶突变,用于有针对性的治疗策略.
主要方法:
- 开发了Kinome-AI,这是一个集成式机器学习框架,利用多模式功能.
- 结合了残留水平的生物化学变化,蛋白质语言模型序列嵌入和分子建模结构描述器.
- 采用教师-学生学习策略来归因缺失的结构数据,利用可用的结构信息.
主要成果:
- 在110个激酶中,Kinome-AI在1003个突变中实现了0.85的AUROC和0.76的BACC.
- 该模型显著优于现有的生物信息学和通用变异效应预测器.
- 归算策略提高了性能,而不需要对新突变进行结构输入.
结论:
- 基诺姆-AI提供了一种可靠的方法来预测基因酶突变激活状态.
- 这个框架量化了与癌症相关的激酶突变中的序列结构功能关系.
- 通过告知有针对性的治疗决策,Kinome-AI有望推进个性化癌症治疗.
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