Authors
Beibei Wang, Siyuan Song, Song Cheng, Yihang Lin, Liang Yu, Yan Li, Xiang Chen
Published in
Frontiers in molecular biosciences. Volume 13. Pages 1761673. Epub Apr 30, 2026.
Abstract
The rapid expansion of whole-genome sequencing (WGS) has highlighted the important contribution of noncoding variants to human disease, yet their pathogenic mechanisms remain difficult to resolve. Traditional statistical and experimental approaches often struggle to capture complex regulatory interactions or establish causal links, leaving many noncoding variants classified as variants of uncertain significance in clinical databases. Recent advances in computational modeling have substantially improved pathogenicity prediction by integrating genomic, epigenetic, and structural information. In parallel, genome language model (gLM)-inspired methods have enabled more context-aware interpretation of noncoding sequences and improved model generalization. This review summarizes current computational approaches, data modalities, and evaluation strategies for noncoding variant pathogenicity prediction, discusses key challenges in interpretability and data heterogeneity, and highlights emerging opportunities for clinical translation.
PMID:
42148149
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.
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