Reference : LAITOR--Literature Assistant for Identification of Terms co-Occurrences and Relationships.
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/17516
LAITOR--Literature Assistant for Identification of Terms co-Occurrences and Relationships.
English
Barbosa Da Silva, Adriano mailto [European Molecular Biology Laboratory - EMBL]
Soldatos, Theodoros G. [> >]
Magalhaes, Ivan L. F. [> >]
Pavlopoulos, Georgios A. [> >]
Fontaine, Jean-Fred [> >]
Andrade-Navarro, Miguel A. [> >]
Schneider, Reinhard mailto [European Molecular Biology Laboratory - EMBL]
Ortega, J. Miguel [> >]
2010
BMC bioinformatics
11
70
Yes (verified by ORBilu)
International
1471-2105
1471-2105
England
[en] Computational Biology/methods ; Data Mining/methods ; Information Storage and Retrieval/methods ; MEDLINE ; Publications ; Software ; United States
[en] BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
http://hdl.handle.net/10993/17516

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