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可解释的机器学习确定了多基因风险评分作为胰腺癌风险的关键预测因素在英国生物银行

Giulia Peduzzi¹, Alessio Felici¹, Roberto Pellungrini²

¹Department of Biology, University of Pisa, Via Luca Ghini, 13 - 56126, Pisa, Italy.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

|December 4, 2024

概括

开发准确的胰腺癌风险预测是至关重要的. 结合基因的机器学习模型,如多基因风险得分 (PRS),显示出确定高风险个体的前景.

科学领域:

在瘤学瘤学.
遗传学遗传学是一个
计算生物学计算生物学

背景情况:

胰腺管腺癌 (PDAC) 的死亡率很高,因此需要改进风险预测.
目前的模型缺乏遗传因素,精度有限.
基因组和环境因素是理解PDAC风险的关键.

研究的目的:

开发一个可解释的机器学习 (ML) 模型来预测PDAC风险.
将遗传信息,特别是多基因风险评分 (PRS) 整合到PDAC风险评估中.
提高PDAC风险预测的准确性和临床实用性.

主要方法:

评估了五种ML模型 (自适应提升,极端梯度提升,CatBoost,深森林,随机森林).
模型使用了来自英国生物库的56个暴露组变量和654个PDAC病例中的PRS和1,308个对照.
为了模型的可解释性,使用了夏普利添加式扩展 (SHAP) 和通过递归分区 (Girp) 进行全球模型解释.

主要成果:

所有测试的ML模型都显示了可比性能.
CatBoost实现了最高的召回率,达到77.10%.
SHAP和Girp认为年龄,PRS和胰腺炎是区分PDAC病例与对照病例的关键预测因素.

结论:

开发的ML模型显示在PDAC风险预测中具有很强的临床应用潜力.
多基因风险评分 (PRS) 是识别PDAC高风险个体的关键因素.
模型解释性方法证实了遗传和临床因素在风险评估中的重要性.

关键词:

可解释的人工智能胰腺癌是一种胰腺癌.多基因风险评分多基因风险评分风险预测风险预测

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