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

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...

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Hauptverfasser: Gonzalo-Calvo, David de (VerfasserIn) , Karaduzovic-Hadziabdic, Kanita (VerfasserIn) , Dalgaard, Louise Torp (VerfasserIn) , Dieterich, Christoph (VerfasserIn) , Perez-Pons, Manel (VerfasserIn) , Hatzigeorgiou, Artemis (VerfasserIn) , Devaux, Yvan (VerfasserIn) , Kararigas, Georgios (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 18 July 2024
In: EBioMedicine
Year: 2024, Jahrgang: 106, Pages: 1-16
ISSN:2352-3964
DOI:10.1016/j.ebiom.2024.105247
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ebiom.2024.105247
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2352396424002834
Volltext
Verfasserangaben:David de Gonzalo-Calvo, Kanita Karaduzovic-Hadziabdic, Louise Torp Dalgaard, Christoph Dieterich, Manel Perez-Pons, Artemis Hatzigeorgiou, Yvan Devaux, and Georgios Kararigas
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 24.02.2025
Beschreibung:Online Resource
ISSN:2352-3964
DOI:10.1016/j.ebiom.2024.105247

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