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

Aug 5, 2026

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The human transcriptome is largely composed of noncoding RNAs (ncRNAs), which regulate many disease-relevant pathophysiological processes and provide a valuable source of next-generation biomarkers. Integrating ncRNA transcripts with clinical records and multi-omics data has driven the adoption of artificial intelligence and machine learning methods. Due to biological complexity, ML is becoming central to ncRNA research, including the discovery of clinically relevant biomarkers and elucidation of ncRNA-associated pathogenetic mechanisms. The article reviews key opportunities and challenges, addresses methodological and conceptual constraints, and discusses ethical considerations for AI in healthcare and research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Summary

Background and rationale

Methods and key focus

Challenges and constraints

Ethical considerations

Clinical implications

Roskilde University Machine learning for catalysing the integration of noncoding RNA in research and clinical practice de Gonzalo-Calvo, David; Karaduzovic-Hadziabdic, Kanita; Dalgaard, Louise Torp; Dieterich, Christoph; Perez-Pons, Manel; Hatzigeorgiou, Artemis; Devaux, Yvan; Kararigas, Georgios Published in: eBioMedicine DOI: 10.1016/j.ebiom.2024.105247 Publication date: 2024 Document Version Publisher's PDF, also known as Version of record Citation for published version (APA): de Gonzalo-Calvo, D. , Karaduzovic-Hadziabdic, K. , Dalgaard, L. T. , Dieterich, C. , Perez-Pons, M. , Hatzigeorgiou, A. , Devaux, Y. , & Kararigas, G. (2024) . Machine learning for catalysing the integration of noncoding RNA in research and clinical practice. eBioMedicine , 106, Article 105247. [https://doi.org/10.1016/j.ebiom.2024.105247](https://doi.org/10.1016/j.ebiom.2024.105247) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain. • You may freely distribute the URL identifying the publication in the public portal. Take down policy If you believe that this document breaches copyright please contact [rucforsk@kb.dk](rucforsk@kb.dk) providing details, and we will remove access to the work immediately and investigate your claim. Download date: 10. Jan. 2025 Review Machine learning for catalysing the integration of noncoding RNA in research and clinical practice David de Gonzalo-Calvo,a,b,∗ Kanita Karaduzovic-Hadziabdic,c Louise Torp Dalgaard,d Christoph Dieterich,e,f Manel Perez-Pons,a,b Artemis Hatzigeorgiou,g,h Yvan Devaux,i and Georgios Kararigasj,∗∗ aTranslational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain bCIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain cFaculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina dDepartment of Science and Environment, Roskilde University, Roskilde, Denmark eKlaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Germany fGerman Center for Cardiovascular Research (DZHK) - Partner Site Heidelberg/Mannheim, Germany gDIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece hHellenic Pasteur Institute, Athens, Greece iCardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg jDepartment of Physiology, Faculty of Medicine, University of Iceland, 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 const

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What ethical considerations are highlighted for AI applications in healthcare and research?

The article discusses ethical considerations inherent to AI applications in both healthcare and research contexts, alongside other constraints, to support responsible deployment of these tools.

Aug 5, 2026

Upload by Oliver