Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration.

Journal: Tuberculosis (Edinburgh, Scotland)
PMID: 39418810

Abstract

OBJECTIVE: The asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene expression screening with machine learning, evaluating their diagnostic potential and correlation with immune cell infiltration.

Authors

  • Shoupeng Ding
    Department of Laboratory Medicine, Gutian County Hospital, Gutian, 352200, China.
  • Xiaomei Yi
    College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
  • Jinghua Gao
    Chuxiong Yi Autonomous Prefecture People's Hospital, Chuxiong, 675000, China.
  • Chunxiao Huang
    Department of Laboratory Medicine, Gutian County Hospital, Gutian, 352200, China.
  • Yuyang Zhou
    Department of Medical Laboratory, Siyang Hospital, Siyang County, 237000, Jiangsu Province, China.
  • Yimei Yang
    Department of Microbiology and Immunology, School of Basic Medical Sciences, Dali University, Dali, 671000, China.
  • Zihan Cai
    Department of Medical Laboratory, Siyang Hospital, Siyang County, 237000, Jiangsu Province, China. zihancai001@163.com.

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