Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

Journal: EBioMedicine
PMID: 39029428

Abstract

The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Authors

  • David de Gonzalo-Calvo
    Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain. Electronic address: dgonzalo@irblleida.cat.
  • Kanita Karaduzovic-Hadziabdic
    Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
  • Louise Torp Dalgaard
    Department of Science and Environment, Roskilde University, Roskilde, Denmark.
  • Christoph Dieterich
    Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg.
  • Manel Perez-Pons
    Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.
  • Artemis Hatzigeorgiou
    DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece; Hellenic Pasteur Institute, Athens, Greece.
  • Yvan Devaux
    Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B rue Edison, L-1445 Strassen, Luxembourg.
  • Georgios Kararigas
    Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland. Electronic address: georgekararigas@gmail.com.

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