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Biomedical text mining

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Title: Biomedical text mining  
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Biomedical text mining

Biomedical text mining (also known as BioNLP) refers to text mining applied to texts and literature of the biomedical and molecular biology domain. It is a rather recent research field on the edge of natural language processing, bioinformatics, medical informatics and computational linguistics.

There is an increasing interest in text mining and information extraction strategies applied to the biomedical and molecular biology literature due to the increasing number of electronically available publications stored in databases such as PubMed.

Contents

  • Main applications 1
  • Conferences at which BioNLP research is presented 2
  • See also 3
  • External links 4
  • References 5
  • Further reading 6

Main applications

The main developments in this area have been related to the identification of biological entities (named entity recognition), such as protein and gene names as well as chemical compounds and drugs [1] in free text, the association of gene clusters obtained by microarray experiments with the biological context provided by the corresponding literature, automatic extraction of protein interactions and associations of proteins to functional concepts (e.g. gene ontology terms). Even the extraction of kinetic parameters from text or the subcellular location of proteins have been addressed by information extraction and text mining technology. Information extraction and text mining methods have been explored to extract information related to biological processes and diseases.[2]

Conferences at which BioNLP research is presented

BioNLP is presented at a variety of meetings:

See also

External links

  • Bio-NLP resources, systems and application database collection
  • The BioNLP mailing list archives
  • Corpora for biomedical text mining
  • The BioCreative evaluations of biomedical text mining technologies
  • Directory of people involved in BioNLP

References

  1. ^ M Krallinger, F Leitner, O Rabal, M Vazquez, J Oyarzabal and A Valencia, Overview of the chemical compound and drug name recognition (CHEMDNER) task. Proceedings of the Fourth BioCreative Challenge Evaluation Workshop vol. 2. 6-37. http://www.biocreative.org/media/store/files/2013/bc4_v2_1.pdf
  2. ^ Krallinger, M; Leitner, F; Valencia, A (2010). "Analysis of Biological Processes and Diseases Using Text Mining Approaches". Bioinformatics Methods in Clinical Research. Methods in Molecular Biology 593. pp. 341–82.  

Further reading

  • Krallinger M, Valencia A (2005). "Text-mining and information-retrieval services for molecular biology". Genome Biol. 6 (7): 224.  
  • Hoffmann R, Krallinger M, Andres E, Tamames J, Blaschke C, Valencia A (May 2005). "Text mining for metabolic pathways, signaling cascades, and protein networks". Sci. STKE 2005 (283): pe21.  
  • Krallinger M, Erhardt RA, Valencia A (March 2005). "Text-mining approaches in molecular biology and biomedicine". Drug Discov. Today 10 (6): 439–45.  
  • Biomedical Literature Mining Publications (BLIMP): A comprehensive and regularly updated index of publications on (bio)medical text mining
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