NCGSn
NCGSn is a theoretical framework used in computational biology and network science to model regulatory signals arising from non-coding regions of the genome. The acronym commonly denotes a graph-based representation of non-coding genome elements and their regulatory influences, focusing on how non-coding signals contribute to gene regulation.
In NCGSn, fundamental elements include non-coding genome segments (NCGs) such as enhancers, silencers, and promoters tied
Common analytical approaches in NCGSn include graph-based inference, graph neural networks, and motif- or feature-based priors
Applications span functional genomics, disease variant interpretation, and systems biology studies of gene regulatory architecture. Challenges