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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
Mining and integration of pathway diagrams from imaging data
Sergey Kozhenkov1, Michael Baitaluk
1San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA.
Bioinformatics (Oxford, England)
|January 24, 2012
Summary
This study introduces a new tool to extract and analyze pathway diagrams from images, making complex biological information more accessible. The BiologicalNetworks resource integrates these pathways with extensive biological data for comprehensive research.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Pathway diagrams from PubMed and the World Wide Web (WWW) contain rich, curated biological information.
- Existing tools lack the capability to analyze pathway images, extract components, and determine relationships.
Purpose of the Study:
- To develop a resource for extracting and analyzing pathway diagrams from images.
- To integrate these pathways into a comprehensive biological knowledgebase.
Main Methods:
- Utilized optical character recognition (OCR) to retrieve pathway diagrams from images.
- Employed data mining and integration methods for pathway recognition.
- Integrated recognized pathways into the BiologicalNetworks research environment.
Main Results:
- Developed a resource of pathway diagrams extracted from article and web-page images.
- Integrated pathways into the BiologicalNetworks knowledgebase, linking to over 100 public data sources.
- Provided multiple search and analytical tools for studying pathways within integrated knowledge.
Conclusions:
- The BiologicalNetworks resource facilitates the study of cellular pathways and molecular networks.
- Enables researchers to analyze biological pathways in the context of integrated knowledge and experimental data.
- BiologicalNetworks software and pathway repository are freely available.
