Paper Info
Title
How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience.
Abstract
There is an increasing interest in developing ontologies and controlled vocabularies to improve the efficiency and consistency of manual literature curation, to enable more formal biocuration workflow results and ultimately to improve analysis of biological data. Two ontologies that have been successfully used for this purpose are the Gene Ontology (GO) for annotating aspects of gene products and the Molecular Interaction ontology (PSI-MI) used by databases that archive protein-protein interactions. The examination of protein interactions has proven to be extremely promising for the understanding of cellular processes. Manual mapping of information from the biomedical literature to bio-ontology terms is one of the most challenging components in the curation pipeline. It requires that expert curators interpret the natural language descriptions contained in articles and infer their semantic equivalents in the ontology (controlled vocabulary). Since manual curation is a time-consuming process, there is strong motivation to implement text-mining techniques to automatically extract annotations from free text. A range of text mining strategies has been devised to assist in the automated extraction of biological data. These strategies either recognize technical terms used recurrently in the literature and propose them as candidates for inclusion in ontologies, or retrieve passages that serve as evidential support for annotating an ontology term, e. g. from the PSI-MI or GO controlled vocabularies. Here, we provide a general overview of current text-mining methods to automatically extract annotations of GO and PSI-MI ontology terms in the context of the BioCreative (Critical Assessment of Information Extraction Systems in Biology) challenge. Special emphasis is given to protein-protein interaction data and PSI-MI terms referring to interaction detection methods.
Year
DOI
Venue
2012
10.1093/database/bas017
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Keywords
Field
DocType
natural language processing,proteomics,data mining
Ontology (information science),Data mining,Biological data,Ontology,Process ontology,Information retrieval,Open Biomedical Ontologies,Computer science,Controlled vocabulary,Data curation,Information extraction,Bioinformatics
Journal
Volume
ISSN
Citations 
2012
1758-0463
11
PageRank 
References 
Authors
0.90
43
8
Name
Order
Citations
PageRank
Martin Krallinger176335.65
Florian Leitner236214.92
Miguel Vazquez32288.54
David Salgado42469.78
Christophe Marcelle5272.05
Mike Tyers6135368.62
Alfonso Valencia72577322.43
Andrew Chatr-aryamontri8112356.83