A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.

Journal: International journal of clinical oncology
PMID: 39292320

Abstract

BACKGROUND: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites.

Authors

  • Marco A De Velasco
    Department of Genome Biology, Faculty of Medicine, Kindai University, Ohnohigashi 377-2, Osaka-Sayama, 589-9511, Japan.
  • Kazuko Sakai
    Department of Genome Biology, Faculty of Medicine, Kindai University, Ohnohigashi 377-2, Osaka-Sayama, 589-9511, Japan.
  • Seiichiro Mitani
    Department of Medical Oncology, Faculty of Medicine, Kindai University, Osaka-Sayama, Japan.
  • Yurie Kura
    Department of Genome Biology, Faculty of Medicine, Kindai University, Ohnohigashi 377-2, Osaka-Sayama, 589-9511, Japan.
  • Shuji Minamoto
    Department of Molecular Tumor Pathobiology, Kindai University Graduate School of Medical Sciences, Osaka-Sayama, Japan.
  • Takahiro Haeno
    Department of Molecular Tumor Pathobiology, Kindai University Graduate School of Medical Sciences, Osaka-Sayama, Japan.
  • Hidetoshi Hayashi
    Department of Medical Oncology, Kindai University Faculty of Medicine, Osaka, Japan. Electronic address: hidet31@med.kindai.ac.jp.
  • Kazuto Nishio
    Department of Genome Biology, Faculty of Medicine, Kindai University, Ohnohigashi 377-2, Osaka-Sayama, 589-9511, Japan. knishio@med.kindai.ac.jp.

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