STCDB4ND: a signal transduction classification database for neurological diseases

• DOI: 10.1093/database/baaf032
• Publication Date: 2025-05-02T16:37:11Z
• Authors: Boyan Gong; Sida Li; Yifan Chen; Liya Liu; Ralf Hofestädt; Ming Chen

Abstract Neurological disorders pose significant global health challenges due to their complex etiology and insufficient understanding of underlying mechanisms. Signal transduction pathways are critical in the pathophysiology of these diseases and have been extensively studied to develop therapeutic interventions. However, existing databases for biological signal pathways often overlook the dynamic interactions between entities within these pathways and lack standardized representations of the signaling processes. To address these limitations, we present STCDB4ND, a specialized database focused on signal transduction pathways associated with neurological diseases. Utilizing the ST classification system, STCDB4ND provides a unified framework for pathway representation, emphasizing interactions and pathway characteristics. The database features advanced visualization tools, network analysis capabilities, and a key factor identification module, enabling researchers to comprehensively study these complex networks. Our analysis of neurological disease-related pathways using STCDB4ND revealed key signaling factors and supported existing findings on pathogenic mechanisms STCDB4ND serves as a valuable resource for advancing the understanding of neurological disease pathways and promoting novel therapeutic approaches. And we believe that STCDB will provide greater convenience for researchers in various fields as we expand the STCDB system’s database in the future. Database URL: https://bis.zju.edu.cn/STCDB