Biomedical text mining

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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.


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[edit] 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 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 systems.

[edit] Examples

  • Chilibot: A tool for finding relationships between genes or gene products.
  • Information Hyperlinked Over Proteins (iHOP) (ref.: Bioinformatics, 2005 Sep 1;21 Suppl 2:ii252-ii258.): "A network of concurring genes and proteins extends through the scientific literature touching on phenotypes, pathologies and gene function. iHOP provides this network as a natural way of accessing millions of PubMed abstracts. By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource, bringing all advantages of the internet to scientific literature research."
  • FABLE: A gene-centric text-mining search engine for MEDLINE

[edit] References

[edit] Conferences at which BioNLP research is presented

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[edit] External links