Leveraging explainable AI for gut microbiome-based colorectal cancer classification

被引：18

作者：

Rynazal, Ryza ^{[1
]}

Fujisawa, Kota ^{[1
]}

Shiroma, Hirotsugu ^{[1
]}

Salim, Felix ^{[1
]}

Mizutani, Sayaka ^{[1
]}

Shiba, Satoshi ^{[5
]}

Yachida, Shinichi ^{[5
,6
]}

Yamada, Takuji ^{[1
,2
,3
,4
]}

机构：

[1] Tokyo Inst Technol, Sch Life Sci & Technol, Tokyo, Japan

[2] Metagen Inc, Yamagata, Japan

[3] Metagen Theurapeut Inc, Yamagata, Japan

[4] Digzyme Inc, Tokyo, Japan

[5] Natl Canc Ctr, Div Genom Med, Tokyo, Japan

[6] Osaka Univ, Grad Sch Med, Dept Canc Genome Informat, Osaka, Japan

来源：

GENOME BIOLOGY | 2023年 / 24卷 / 01期

基金：

日本学术振兴会;

关键词：

D O I：

10.1186/s13059-023-02858-4

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.

引用

页数：13

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