Machine learning for catalysing the integration of noncoding RNA in research and clinical practice
David de Gonzalo-Calvo,
Kanita Karaduzovic-Hadziabdic,
Louise Torp Dalgaard,
Christoph Dieterich,
Manel Perez-Pons,
Artemis Hatzigeorgiou,
Yvan Devaux,
Georgios Kararigas
Affiliations
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; Corresponding author. Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Avda. Alcalde Rovira Roure 80, 25198, Lleida, Spain.
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
Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Germany; German Center for Cardiovascular Research (DZHK) - Partner Site Heidelberg/Mannheim, Germany
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 Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
Georgios Kararigas
Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland; Corresponding author. Department of Physiology, Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland.
Summary: 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.
