Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.

Journal: Journal of ovarian research
Published Date: Jan 3, 2025

Abstract

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models. To address this research gap, we have increased the sample size in our study and aim to utilize two ML algorithms to analyze and validate diagnostic biomarkers, as well as explore immune cell infiltration patterns in PCOS.

Authors

  • Wenxiu Chen
    Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Jianliang Miao
    First Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian, China.
  • Jingfei Chen
    Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. jingfeichen@csu.edu.cn.
  • Jianlin Chen
    Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. jianlinchen@csu.edu.cn.

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