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Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .
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http://dx.doi.org/10.1038/s41540-024-00330-y | DOI Listing |
Mediators Inflamm
September 2025
Shengli Clinical Medical College of Fujian Medical University, Fuzhou 350001, Fujian, China.
Osteoporosis is a prevalent metabolic bone disorder with complex molecular underpinnings. Emerging evidence implicates endoplasmic reticulum stress (ERS) in its pathogenesis; however, systematic exploration of ERS-related genes (ERSRGs) remains limited. This study aimed to identify ERS-related differentially expressed genes (ERSRDEGs) in osteoporosis, construct a diagnostic model, and elucidate associated molecular mechanisms.
View Article and Find Full Text PDFPLoS One
September 2025
Biology Department, Reed College, Portland, Oregon, United States of America.
Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein's position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions.
View Article and Find Full Text PDFJ Biosci
September 2025
Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur 784028, India.
Plants, being sessile organisms, are continually exposed to diverse combinations of biotic and abiotic stresses in their natural habitats. Combined stresses are considered significant threats to plants, particularly in the context of today's climate change. Despite the vast amount of data generated by OMICS experiments, information remains widely dispersed and inaccessible within the literature.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University Mainz, Germany.
This is a corrigendum to our GMDS 2024 article "Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data", published in Studies in Health Technology and Informatics, Volume 317 (IOS Press). The corrigendum improves the readability of the article and has no implications regarding the results or the conclusions of the original work.
View Article and Find Full Text PDFMedicine (Baltimore)
August 2025
Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China.
This study aims to explore the mechanism of artemisinin in treating osteoarthritis (OA) through bioinformatics and network pharmacology. The targets of artemisinin were obtained from databases such as TCMSP, and the disease targets of OA were screened from OMIM, TTD, DisGeNET, and GEO databases. The predicted targets of artemisinin were intersected with OA disease targets to obtain drug-disease common targets, which were visualized using a Venn diagram.
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