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

BMC Bioinformatics. 2010 Feb 1;11:70. doi: 10.1186/1471-2105-11-70.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods
  • Data Mining / methods*
  • Information Storage and Retrieval / methods*
  • MEDLINE
  • Publications
  • Software*
  • United States