Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

Journal: World journal of pediatrics : WJP
PMID: 38401044

Abstract

INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA.

Authors

  • Zhi-Xing Zhu
    Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Center for Biomedical Informatics, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Georgi Z Genchev
    Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics and Data Science, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • Yan-Min Wang
    Newborn Screening Center, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Wei Ji
  • Yong-Yong Ren
    SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Guo-Li Tian
    Newborn Screening Center, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. tiangl@shchildren.com.cn.
  • Sira Sriswasdi
    Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Hui Lu
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.

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