利用可解释的人工智能技术对肠道微生物组基于结直肠癌进行分类。
Leveraging explainable AI for gut microbiome-based colorectal cancer classification.
发表日期：2023 Feb 09
作者：
Ryza Rynazal, Kota Fujisawa, Hirotsugu Shiroma, Felix Salim, Sayaka Mizutani, Satoshi Shiba, Shinichi Yachida, Takuji Yamada
来源：
GENOME BIOLOGY
摘要：
研究显示，结直肠癌（CRC）与肠道菌群成分之间存在联系。在这些研究中，利用全局解释方法使用机器学习推断CRC生物标志物。尽管这些方法允许识别通常与CRC相关的细菌，但它们无法识别对某些个体有影响的物种。在本研究中，我们研究Shapley加性解释（SHAP）在更个性化的CRC生物标志物识别中的潜力。对五个独立数据集的分析表明，该方法甚至可以将CRC患者分为具有不同CRC概率和细菌生物标志物的亚组。© 2023.作者（S）。
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.© 2023. The Author(s).