Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Machine learning enables identification of ncRNA signatures with high predictive accuracy for cancer diagnosis and prognosis, advancing early detection efforts.

• Why it matters: Cancer's biological heterogeneity and the complexity of ncRNA datasets hinder clinical translation of ncRNA-based diagnostics, risking delayed or inaccurate detection.
• What they did: The review analyzes ML frameworks applied to four ncRNA subclasses—miRNAs, lncRNAs, circRNAs, and piRNAs—highlighting methodological progress and persistent challenges.
• The result: Developing validated, interpretable ML models for ncRNA biomarkers could transform early cancer detection and personalized treatment, potentially reducing the global cancer burden.