LAITOR--Literature Assistant for Identification of Terms co-Occurrences and Relationships

Biological knowledge is represented in scientific literature that often describes the function of genes/proteins (bioentities) in terms of their interactions (biointeractions). Such bioentities are often related to biological concepts of interest that are specific of a determined research field. The...

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Bibliographic Details
Published in:BMC bioinformatics 2010-02, Vol.11 (1), p.70-70, Article 70
Main Authors: Barbosa-Silva, Adriano, Soldatos, Theodoros G, Magalhães, Ivan L F, Pavlopoulos, Georgios A, Fontaine, Jean-Fred, Andrade-Navarro, Miguel A, Schneider, Reinhard, Ortega, J Miguel
Format: Article
Language:English
Subjects:
Computational Biology - methods
Data mining
Data Mining - methods
Information management
Information Storage and Retrieval - methods
Management
MEDLINE
Methods
Physiological aspects
Protein-protein interactions
Scientific literature
Software
United States
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Summary:Biological knowledge is represented in scientific literature that often describes the function of genes/proteins (bioentities) in terms of their interactions (biointeractions). Such bioentities are often related to biological concepts of interest that are specific of a determined research field. Therefore, the study of the current literature about a selected topic deposited in public databases, facilitates the generation of novel hypotheses associating a set of bioentities to a common context. We created a text mining system (LAITOR: Literature Assistant for Identification of Terms co-Occurrences and Relationships) that analyses co-occurrences of bioentities, biointeractions, and other biological terms in MEDLINE abstracts. The method accounts for the position of the co-occurring terms within sentences or abstracts. The system detected abstracts mentioning protein-protein interactions in a standard test (BioCreative II IAS test data) with a precision of 0.82-0.89 and a recall of 0.48-0.70. We illustrate the application of LAITOR to the detection of plant response genes in a dataset of 1000 abstracts relevant to the topic. Text mining tools combining the extraction of interacting bioentities and biological concepts with network displays can be helpful in developing reasonable hypotheses in different scientific backgrounds.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-11-70