AUTHOR=Zou Yongfu , Lu Yusong , Lu Shanghui , Wei Zhanliang , Li Le , Liao Shuilin , Zeng Ting , Zhang Yi , Miao Rui 

TITLE=CGSDA: inferring snoRNA-disease associations via ChebNetII and GatedGCN

JOURNAL=Frontiers in Genetics

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1684484

DOI=10.3389/fgene.2025.1684484

ISSN=1664-8021

ABSTRACT=IntroductionRecent biomedical studies have highlighted the pivotal role of non-coding RNAs (ncRNAs) in gene regulatory networks, where they influence gene expression, cellular function, and the onset and progression of various diseases. Among these, small nucleolar RNAs (snoRNAs), a prominent class of small ncRNAs, have attracted considerable research attention over the past two decades. Initially recognized for their involvement in rRNA processing and modification, snoRNAs are now understood to contribute to broader biological processes, including the regulation of disease mechanisms, maintenance of cellular homeostasis, and development of targeted therapeutic strategies. With ongoing advancements, snoRNAs are increasingly regarded as promising candidates for novel therapeutic agents in cancer, neurodegenerative disorders, endocrine conditions, and cardiovascular diseases. Consequently, there is a growing demand for efficient, cost-effective, and environment-independent approaches to study snoRNAs, which has driven the adoption of computational methodologies in this domain.MethodsIn this work, we propose a novel predictive framework, CGSDA, which integrates a ChebNetII convolutional network with a gated graph sequence neural network to identify potential snoRNA–disease associations. The model begins by constructing a snoRNA–disease association network, embedding residual mechanisms into both modules to effectively capture the representations of snoRNAs and diseases. These representations are then fused and dimensionally reduced, after which the refined embeddings are fed into a predictor to generate association predictions.ResultsExperimental evaluation demonstrates that CGSDA consistently outperforms baseline models in predictive accuracy. Ablation experiments were conducted to assess the contribution of each module, confirming that all components substantially enhance overall performance and validating the robustness of the proposed method. Furthermore, case studies on lung cancer and breast cancer showed that 10 out of the top 15 and 12 out of the top 15 predicted snoRNA-disease associations were validated by existing literature, respectively, confirming the model’s effectiveness in identifying potential novel snoRNA-disease associations.DiscussionThe implementation of CGSDA, along with relevant datasets, is publicly available at: https://github.com/cuntjx/CGSDA. This public release enables the research community to further validate and apply the framework, supporting advancements in computational identification of snoRNA–disease associations and facilitating progress in snoRNA-based therapeutic development, and ultimately benefiting human health.