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Updated: Sep 18, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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在乳腺癌诊断中用于血清基代谢的可解释机器学习:来自多目标特征选择驱动光GBM-SHAP模型的见解.

Emek Guldogan¹, Fatma Hilal Yagin², Hasan Ucuzal¹

¹Department of Biostatistics, and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.

Medicina (Kaunas, Lithuania)

|June 27, 2025

概括

这项研究使用先进的代谢学和可解释的人工智能 (XAI) 确定了用于乳腺癌检测的新型血清代谢生物标志物. 这些发现提高了诊断的准确性,并提供了对疾病进展的生物学见解.

关键词:

轻GBMM 轻GBMM 的时间这就是 SHAP SHAP 的意思.生物标志物生物标志物乳腺癌乳腺癌乳腺癌诊断的准确性诊断的准确性可以解释的人工智能AI 代谢生物组的代谢生物组

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科学领域:

代谢学代谢学代谢学
机器学习机器学习
生物标志物发现发现

背景情况:

乳腺癌是全球妇女死亡的主要原因.
乳腺癌的传统生物标志物缺乏敏感性和特异性,特别是在早期阶段.
代谢学和可解释的人工智能 (XAI) 为改善乳腺癌诊断提供了有希望的途径.

研究的目的:

识别和验证基于血清的代谢生物标志物用于乳腺癌检测.
通过先进的代谢分析和机器学习来提高诊断准确度.
利用夏普利添加式解释 (SHAP) 来实现模型解释性和生物洞察力.

主要方法:

用LC-TOFMS和GC-TOFMS分析了来自103名乳腺癌患者和31名对照患者的血清样本.
用多目标特征选择 (MOFS) 进行了强大的生物标志物发现.
使用SHAP分析的光梯度增强机 (LightGBM) 用于分类和代谢物重要性排名.

主要成果:

在乳腺癌检测中,LightGBM实现了86.6%的准确度,89.1%的灵敏度和84.2%的特异性.
SHAP分析确定了2-氨基黄油酸,胆和甲酸作为关键的有影响力的代谢物.
代谢物失调与乳腺癌风险显著相关.

结论:

这项研究将SHAP可解释性与代谢学相结合,用于增强乳腺癌诊断.
鉴定的生物标志物提高了诊断准确度,并揭示了与乳腺癌相关的代谢失调.
将代谢学与XAI驱动的机器学习相结合,显示出临床采用的巨大潜力.