In Genome biology ; h5-index 114.0
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.
Rynazal Ryza, Fujisawa Kota, Shiroma Hirotsugu, Salim Felix, Mizutani Sayaka, Shiba Satoshi, Yachida Shinichi, Yamada Takuji
2023-Feb-09
