Leveraging explainable AI for gut microbiome-based colorectal cancer classification

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 recogni...

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Published inGenome Biology Vol. 24; no. 1; p. 21
Main Authors Rynazal, Ryza, Fujisawa, Kota, Shiroma, Hirotsugu, Salim, Felix, Mizutani, Sayaka, Shiba, Satoshi, Yachida, Shinichi, Yamada, Takuji
Format Journal Article
LanguageEnglish
Published England BioMed Central 09.02.2023
BMC
Subjects
Algorithms
Artificial Intelligence
Bacteria
Biomarkers
Biomarkers, Tumor
Colorectal cancer
Colorectal carcinoma
colorectal neoplasms
Colorectal Neoplasms - microbiology
data collection
Datasets
Digestive system
Gastrointestinal Microbiome
genome
Gut microbiota
Humans
Intestinal microflora
intestinal microorganisms
Method
Methods
Microbiomes
Microbiota
Patients
species
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Summary: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.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-023-02858-4