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 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.

Examples

• XTractor — Discovering Newer Scientific Relations Across PubMed Abstracts. A tool to obtain manually annotated,expert curated relationships for Proteins, Diseases, Drugs and Biological Processes as they get published in PubMed.
• Chilibot — A tool for finding relationships between genes or gene products.
• Information Hyperlinked Over Proteins (iHOP): "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
• GoPubMed — retrieves PubMed abstracts for your search query, then detects ontology terms from the Gene Ontology and Medical Subject Headings in the abstracts and allows the user to browse the search results by exploring the ontologies and displaying only papers mentioning selected terms, their synonyms or descendants.
• LitInspector — Gene and signal transduction pathway data mining in PubMed abstracts.
• PubGene — Co-occurrence networks display of gene and protein symbols as well as MeSH, GO, PubChem and interaction terms (such as "binds" or "induces") as these appear in MEDLINE records (that is, PubMed titles and abstracts).
• PubAnatomy — An interactive visual search engine that provides new ways to explore relationships among Medline literature, text mining results, anatomical structures, gene expression and other background information.
• NextBio- Life sciences search engine with a text mining functionality that utilizes PubMed abstracts (ex: literature search)and clinical trials (example) to return concepts relevant to the query based on a number of heuristics including ontology relationships, journal impact, publication date, and authorship.
• GOAnnotator, an online tool that uses Semantic similarity for verification of electronic protein annotations using GO terms automatically extracted from literature.
• TexFlame, an online tool that renders a single PubMed abstract as a Systems Biology Graphical Notation (SBGN)-like graph. The graph is a complete syntactic-semantic representation of the abstract.

References

• Krallinger M, Valencia A (2005). "Text-mining and information-retrieval services for molecular biology". Genome Biol. 6 (7): 224. doi:10.1186/gb-2005-6-7-224. PMC 1175978. PMID 15998455.{{cite journal}}: CS1 maint: unflagged free DOI (link)
• Hoffmann R, Krallinger M, Andres E, Tamames J, Blaschke C, Valencia A (2005). "Text mining for metabolic pathways, signaling cascades, and protein networks". Sci. STKE. 2005 (283): pe21. doi:10.1126/stke.2832005pe21. PMID 15886388. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Krallinger M, Erhardt RA, Valencia A (2005). "Text-mining approaches in molecular biology and biomedicine". Drug Discov. Today. 10 (6): 439–45. doi:10.1016/S1359-6446(05)03376-3. PMID 15808823. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
• Biomedical Literature Mining Publications (BLIMP): A comprehensive and regularly updated index of publications on (bio)medical text mining

Conferences at which BioNLP research is presented

BioNLP is presented at a variety of meetings:

• Pacific Symposium on Biocomputing: in plenary session
• Intelligent Systems for Molecular Biology: in plenary session and also in the BioLINK and Bio-ontologies workshops
• Association for Computational Linguistics and North American Association for Computational Linguistics annual meetings and associated workshops: in plenary session and as part of the BioNLP workshop (see below)
• BioNLP 2009
• American Medical Informatics Association annual meeting: in plenary session

See also

• BioCreative
• TREC Genomics

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