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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Detalles Bibliográficos
Autores principales: Rynazal, Ryza, Fujisawa, Kota, Shiroma, Hirotsugu, Salim, Felix, Mizutani, Sayaka, Shiba, Satoshi, Yachida, Shinichi, Yamada, Takuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912568/
https://www.ncbi.nlm.nih.gov/pubmed/36759888
http://dx.doi.org/10.1186/s13059-023-02858-4
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02858-4.

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