Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis.

Journal: BMC medical informatics and decision making
Published Date: Jun 20, 2024

Abstract

BACKGROUND: Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources.

Authors

  • Tanakamol Mahawan
    Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.
  • Teifion Luckett
    Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Ainhoa Mielgo Iza
    Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Natapol Pornputtapong
    Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand. natapol.p@chula.ac.th.
  • Eva Caamaño Gutiérrez
    Department of Biochemistry & System Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. caamano@liverpool.ac.uk.

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