title: Mining and integration of pathway diagrams from imaging data.
abstract: BACKGROUND
  Pathway diagrams from PubMed and World Wide Web (WWW) contain valuable highly curated information difficult to reach without tools specifically designed and customized for the biological semantics and high-content density of the images. There is currently no search engine or tool that can analyze pathway images, extract their pathway components (molecules, genes, proteins, organelles, cells, organs, etc.) and indicate their relationships.
  
  
  RESULTS
  Here, we describe a resource of pathway diagrams retrieved from article and web-page images through optical character recognition, in conjunction with data mining and data integration methods. The recognized pathways are integrated into the BiologicalNetworks research environment linking them to a wealth of data available in the BiologicalNetworks' knowledgebase, which integrates data from >100 public data sources and the biomedical literature. Multiple search and analytical tools are available that allow the recognized cellular pathways, molecular networks and cell/tissue/organ diagrams to be studied in the context of integrated knowledge, experimental data and the literature.
  
  
  BACKGROUND
  BiologicalNetworks software and the pathway repository are freely available at www.biologicalnetworks.org.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA.
authors.forename: Sergey
authors.initials: S
authors.lastname: Kozhenkov
authors.forename: Michael
authors.initials: M
authors.lastname: Baitaluk
mesh_terms: D002477:Cells / Q000378:metabolism; D057225:Data Mining; D006801:Humans; D020407:Internet; D008488:Medical Illustration; D015388:Organelles / Q000378:metabolism; D011506:Proteins / Q000378:metabolism; D039781:PubMed; D015398:Signal Transduction; D012984:Software
chemical_list: D011506:Proteins