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pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer
Author(s) -
Aylwin Ng
Publication year - 2005
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkj044
Subject(s) - biology , computational biology , signalling , protein–protein interaction , gene regulatory network , signal transduction , interaction information , gene , ontology , gene ontology , identification (biology) , regulation of gene expression , transcription factor , interaction network , systems biology , interface (matter) , bioinformatics , gene expression , genetics , microbiology and biotechnology , philosophy , pulmonary surfactant , statistics , mathematics , botany , gibbs isotherm , epistemology , biochemistry
pSTIING (http://pstiing.licr.org) is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein-protein, protein-lipid, protein-small molecule interactions and transcriptional regulatory associations), ligand-receptor-cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface provide graphical representations of networks allowing users to combine and extend transcriptional regulatory and signalling modules, infer molecular interactions across species and explore networks via protein domains/motifs, gene ontology annotations and human diseases. pSTIING also supports the direct cross-correlation of experimental results with interaction information in the knowledgebase via the CLADIST tool associated with pSTIING, which currently analyses and clusters gene expression, proteomic and phenotypic datasets. This allows the contextual projection of co-expression patterns onto prior network information, facilitating the identification of functional modules in physiologically relevant systems.

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