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

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.

Keywords: Artificial intelligence; Biomarker; Machine learning; Molecular pathways; Noncoding RNA; Personalised medicine.

Fig. 1

Noncoding transcriptome and key potential points for molecular phenotyping and biomarker development. Abbreviations: circRNAs, circular RNAs; eRNAs, enhancer RNAs; gRNAs, guide RNAs; lncRNAs, long noncoding RNAs; miRNAs, microRNAs; piRNAs, piwi-interacting RNAs; rRNAs, ribosomal RNAs; siRNAs, small interfering RNAs; snRNA, small nuclear RNA; tRNAs, transfer RNAs.