PID: the Pathway Interaction Database

By C. Schaefer, K. Anthony, Shiva Krupa, Jeffrey Buchoff, Matthew Day, Timo Hannay, K. Buetow. Nucleic Acids Res., 2008

653 citations recorded by the source corpus.

The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/a69e6053a0e717cebfe4bae52df85fe23d39a4aa

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Citations in the corpus

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases (2011, background): “Specifically, KEGG PATHWAY (3) and Reactome (29,30) are general pathway databases, whereas PID (27) and Panther (31) focus on signaling pathways and BioCyc (28) focuses on metabolic pathways.”
Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action (2012, background): “A protein-protein interaction network can be expanded by connecting proteins to their physiological function (20), and, in doing so, one can analyze drug-to-physiological function through combining edges that represent drug-protein, protein-protein, and protein-physiology interactions.”
The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function (2010, background): “Data sets are collected from publicly available databases, including co-expression data from Gene Expression Omnibus (GEO) (1); physical and genetic interaction data from BioGRID (2); predicted protein interaction data based on orthology from I2D (3); and pathway and molecular interaction data from ”
BisoGenet: a new tool for gene network building, visualization and analysis (2010, methodology): “On the other hand databases like KEGG[9], Reactome[10], BioCyc[11], NCI Nature PID[12] and others provide information on both metabolic and signaling pathways.”
Database resources of the National Center for Biotechnology Information (2006, neutral): “Currently, Biosystems receives data from the Kyoto Encyclopedia of Genes and Genomes (35–37), BioCyc (38), Reactome (39), the Pathway Interaction Database (40), WikiPathways (41,42) and Gene Ontology (43).”
Network-based stratification of tumor mutations (2013, background): “In order to estimate the appropriate size of cancer pathways ( s ), we examined the known cancer pathways in the NCI-Nature pathway interaction database 63 .”
The NCBI BioSystems database (2009, background): “Fortunately, a considerable number of resources have been created to address this issue: the Pathguide (1) resource lists nearly 300 pathway resources alone, including KEGG (2), Reactome (3), PID (4), PharmGKB (5), GenMAPP (6), Biocyc (7) and many others.”
Integrated Genomic Analyses of Ovarian Carcinoma (2011, background): “Bernanke(30), Laetitia Borsu(41), Douglas A.”
STITCH 4: integration of protein–chemical interactions with user data (2013, background): “The second channel is made up of manually curated drug–target databases: DrugBank (12), GLIDA (13), Matador (14), TTD (15) and CTD (16); and pathway databases: KEGG (17), NCI/Nature Pathway Interaction Database (18), Reactome (19) and BioCyc (20) .”
EnrichNet: network-based gene set enrichment analysis (2012, methodology): “…reference datasets, the target set is first mapped onto a molecular network (here a connected human interactome graph extracted from the STRING 9.0 database (Snel et al., 2000; Von Mering et al., 2003), with edges weighted by the STRING combined confidence score normalized to range [0,1]).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “nces IPAVS Signaling pathway resources http://ipavs.cidms.org Sreenivasaiah et al., 2012 Reactome http://reactome.org Croft et al., 2011 NCI Nature–Pathway Interaction Database http://pid.nci.nih.gov Schaefer et al., 2009 NetPath http://netpath.org Kandasamy et al., 2010 JASPAR Transcription factor–”
miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets (2012, methodology): “Five pathway databases, including Gene Ontology [26], KEGG [27], BioCarta, Pathway Interaction Database [28], and Reactome [29], were used to annotate the biological functions and canonical pathways of target genes.”
RNA-Seq of Single Prostate CTCs Implicates Noncanonical Wnt Signaling in Antiandrogen Resistance (2015, neutral): “We used the Pathway Interaction Database (PID) (15) to identify key molecular pathways up-regulated in SCIENCE sciencemag.org 18 SEPTEMBER 2015 • VOL 349 ISSUE 6254 1351 1Massachusetts General Cancer Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, background): “Gene pairs not co - annotated to any terms in the GO fringe , KEGG [ 86 ] , PID [ 87 ] or Biocyc [ 88 ] were considered as unrelated ( i . e . negative examples ) except in the following cases : 1 .”