IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network

作者：Xiaowen Hu, Pan Zhang, Dayun Liu, Jiaxuan Zhang, Yuanpeng Zhang, Yihan Dong, Yanhao Fan, Lei Deng · 发表于：Briefings in Bioinformatics · 年份：2024 · DOI：10.1093/bib/bbae179 · 被引用次数：11 · 研究领域：Cancer-related molecular mechanisms research、RNA modifications and cancer、MicroRNA in disease regulation

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence t...