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. 2009 Dec;5(12):e1000597.
doi: 10.1371/journal.pcbi.1000597. Epub 2009 Dec 24.

Biomedical text mining and its applications

Raul Rodriguez-Esteban  1
Affiliations

Biomedical text mining and its applications

Raul Rodriguez-Esteban. PLoS Comput Biol. 2009 Dec.
No abstract available

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Conflict of interest statement

The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Examples of term recognition.
(A) Text marked with protein (blue), disease (crimson), Gene Ontology (bright red), chemical (dark red), and species (red) terms by Whatizit with the whatizitEBIMedDiseaseChemicals pipeline. (B) Text marked with protein and cell line terms by ABNER . (C) Protein terms identified by the prototype BIOCreAtIvE metaserver . In the example shown, the metaserver combines the output of systems hosted in three servers.
Figure 2
Figure 2. Example of text-mined PPI network.
The nodes are proteins identified using the query: “leukoencephalopathy, progressive multifocal”[mh] antibody[pubmed] in GoGene . The query retrieves gene symbols mapped to PubMed abstracts that include the keyword antibody and the MeSH term leukoencephalopathy, progressive multifocal (PML). The gene list was exported to SIF format and the gene symbols extracted and used to query PPI using iHOP Web services . Only those iHOP interactions with at least two co-occurrences and confidence above zero were considered. The network was plotted using Cytoscape . The node color is based on the number of interactions (node degree).
See this image and copyright information in PMC

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