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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.
== 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 〔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〕 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.
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