protPheMut:一种可解释的机器学习工具,用于人类误解突变中的癌症和神经发育障碍的分类
Jingran Wang1, Miao Yang1, Chang Zong1
1MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Journal of chemical information and modeling
|July 28, 2025
概括
新的工具protPheMut可以预测蛋白质突变是否会导致癌症或神经发育障碍 (NDD). 它使用可解释的机器学习和网络动态来实现个性化医学的高准确性.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 个性化医疗是个性化的医疗.
背景情况:
- 单个蛋白质中的错误突变可以导致各种表型,包括癌症和神经发育障碍 (NDD).
- 现有的工具很难将特定突变与不同的表型联系起来,这阻碍了个性化医学应用.
- 像PI3Kα,PTEN和RAS这样的瘤蛋白具有与各种癌症和NDD相关的突变.
研究的目的:
- 开发一种新的计算工具,protPheMut,用于预测蛋白质中的错误突变是否会导致癌症或NDD.
- 利用可解释的机器学习和SHAP解释来实现透明和准确的表型预测.
- 整合多样化的生物物理和网络动态签名,以进行增强的突变效应分析.
主要方法:
- 开发了 protPheMut,这是一个集生物物理和网络动态特征的机器学习模型.
- 使用SHAP (夏普利添加式解释) 进行模型解释性和特征重要性分析.
- 使用交叉验证和独立测试集验证了protPheMut,包括PI3Kα和PTEN突变的案例研究.
主要成果:
- 在区分癌症与NDD相关突变方面,protPheMut获得了很高的准确性 (交叉验证中的AUCROC为0.9118,测试组中的0.8925).
- 与其他七种工具相比,在预测PI3Kα和PTEN突变的表型效应方面表现出卓越的性能.
- 获得PI3Kα突变 (癌症/考登综合征) 的AUROC为0.8501,PTEN突变 (癌症/PHTS/HCPS) 的AUROC为0.9349.
结论:
- protPheMut能够准确地预测由误解突变引起的疾病表型 (癌症/NDD).
- 可解释机器学习,特别是SHAP解释,突出了网络和动态特征在表型分类中的重要性.
- 该工具通过将特定突变与不同的疾病结果联系起来,为个性化医学提供了宝贵的资源.
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