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<p>Genetic and microbial factors influence inflammatory bowel disease (IBD), prompting our study on non-invasive biomarkers for enhanced diagnostic precision. Using the XGBoost algorithm and variable analysis and the published metadata, we developed the 10-species signature XGBoost classification model (XGB-IBD10). By using distinct species signatures and prior machine and deep learning models and employing standardization methods to ensure comparability between metagenomic and 16S sequencing data, we constructed classification models to assess the XGB-IBD10 precision and effectiveness. XGB-IBD10 achieved a notable accuracy of 0.8722 in testing samples. In addition, we generated metagenomic sequencing data from collected 181 stool samples to validate our findings, and the model reached an accuracy of 0.8066. The model's performance significantly improved when trained on high-quality data from the Chinese population. Furthermore, the microbiome-based model showed promise in predicting active IBD. Overall, this study identifies promising non-invasive biomarkers associated with IBD, which could greatly enhance diagnostic accuracy.</p>

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Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月14日

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  • Shicheng Yu,
  • Jun Li,
  • Zhaofeng Ye,
  • Mengxian Zhang,
  • Xiaohua Guo,
  • Xu Wang,
  • Liansheng Liu,
  • Yalong Wang,
  • Xin Zhou,
  • Wei Fu,
  • Michael Q. Zhang,
  • Ye-Guang Chen

摘要

Genetic and microbial factors influence inflammatory bowel disease (IBD), prompting our study on non-invasive biomarkers for enhanced diagnostic precision. Using the XGBoost algorithm and variable analysis and the published metadata, we developed the 10-species signature XGBoost classification model (XGB-IBD10). By using distinct species signatures and prior machine and deep learning models and employing standardization methods to ensure comparability between metagenomic and 16S sequencing data, we constructed classification models to assess the XGB-IBD10 precision and effectiveness. XGB-IBD10 achieved a notable accuracy of 0.8722 in testing samples. In addition, we generated metagenomic sequencing data from collected 181 stool samples to validate our findings, and the model reached an accuracy of 0.8066. The model's performance significantly improved when trained on high-quality data from the Chinese population. Furthermore, the microbiome-based model showed promise in predicting active IBD. Overall, this study identifies promising non-invasive biomarkers associated with IBD, which could greatly enhance diagnostic accuracy.