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微AIbiome:使用可解释的机器学习从微生物档案解码癌症类型

Md Motiur Rahman¹, Shiva Shokouhmand¹, Saeka Rahman¹

¹School of Engineering Technology, Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.

|September 27, 2025

概括

这项研究介绍了MicroAIbiome,这是一个AI管道,用于使用微生物组数据分类五种癌症类型. 它实现了78.23%的准确性,突出了AI.

科学领域:

微生物组研究的研究.
计算生物学是一种计算生物学.
癌症诊断癌症诊断癌症诊断

背景情况:

人体组织微生物群落显示出作为癌症生物标志物的前景.
微生物组数据对多类癌症的分类提出了挑战,原因是复合性,高维度和缺乏可解释性.

研究的目的:

开发一个AI管道 (MicroAIbiome) 用于分类五种癌症类型,使用基因级微生物相对丰度.
解决癌症分类微生物组数据分析方面的挑战.
提高模型的解释性,以发现与特定癌症相关的微生物特征.

主要方法:

开发了MicroAIbiome AI管道,包括零替换,中心日志比率 (CLR) 转换,相关性过和递归特征消除 (RFE).
评估了五个机器学习分类器,其中XGBoost表现最好.
利用夏普利添加剂扩展 (SHAP) 进行特征归属和微生物特征识别.

主要成果:

微AIbiome管道成功分类了五种癌症类型:食道癌 (ESCA),头角状细胞癌 (HNSC),胃腺癌 (STAD),结肠腺癌 (COAD) 和直肠腺癌 (READ).
XGBoost分类器实现了最高准确率78.23%,超过了之前的研究.
通过SHAP分析确定了特定类别的微生物特征,包括ESCA的Corynebacterium和COAD的Bacteroides.

结论:

组合数据预处理和可解释的AI对于推进基于微生物组的癌症诊断至关重要.
微AIbiome管道为使用微生物组数据进行多类癌症分类提供了强大的和可解释的方法.
微生物组签名对早期癌症检测和个性化医学具有重大潜力.

关键词:

在SHAP中,价值是SHAP值.癌症分类癌症分类癌症分类机器学习是机器学习.微生物的签名微生物的签名微生物组是一个微生物组.

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