LAITOR--Literature Assistant for Identification of Terms co-Occurrences and Relationships.
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.
Barbosa-Silva
Adriano
A
Max-Delbrück Center for Molecular Medicine, Berlin, Germany.
Soldatos
Theodoros G
TG
Magalhães
Ivan L F
IL
Pavlopoulos
Georgios A
GA
Fontaine
Jean-Fred
JF
Andrade-Navarro
Miguel A
MA
Schneider
Reinhard
R
Ortega
J Miguel
JM
D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D011642:Publications; D012984:Software; D014481:United States