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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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
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author Rynazal, Ryza
Fujisawa, Kota
Shiroma, Hirotsugu
Salim, Felix
Mizutani, Sayaka
Shiba, Satoshi
Yachida, Shinichi
Yamada, Takuji
author_facet Rynazal, Ryza
Fujisawa, Kota
Shiroma, Hirotsugu
Salim, Felix
Mizutani, Sayaka
Shiba, Satoshi
Yachida, Shinichi
Yamada, Takuji
author_sort Rynazal, Ryza
collection PubMed
description 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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spelling pubmed-99125682023-02-11 Leveraging explainable AI for gut microbiome-based colorectal cancer classification Rynazal, Ryza Fujisawa, Kota Shiroma, Hirotsugu Salim, Felix Mizutani, Sayaka Shiba, Satoshi Yachida, Shinichi Yamada, Takuji Genome Biol Method 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. BioMed Central 2023-02-09 /pmc/articles/PMC9912568/ /pubmed/36759888 http://dx.doi.org/10.1186/s13059-023-02858-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Rynazal, Ryza
Fujisawa, Kota
Shiroma, Hirotsugu
Salim, Felix
Mizutani, Sayaka
Shiba, Satoshi
Yachida, Shinichi
Yamada, Takuji
Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title_full Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title_fullStr Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title_full_unstemmed Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title_short Leveraging explainable AI for gut microbiome-based colorectal cancer classification
title_sort leveraging explainable ai for gut microbiome-based colorectal cancer classification
topic Method
url 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
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