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Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

2024; Frontiers Media; Volume: 15; Linguagem: Inglês

10.3389/fmicb.2024.1348974

1664-302X

Pierfrancesco Novielli, Donato Romano, Michele Magarelli, Pierpaolo Di Bitonto, Domenico Diacono, Annalisa Chiatante, Giuseppe Lopalco, D. V. A. Sabella, Vincenzo Venerito, Pasquale Filannino, R. Bellotti, Maria De Angelis, Florenzo Iannone, Sabina Tangaro,

Machine Learning in Healthcare

Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.