Assisting medical annotation in Swiss-Prot using statistical classifiers

Bio-medical knowledge bases are valuable resources for the research community. Original scientific publications are the main source used to annotate them. Medical annotation in Swiss-Prot is specifically targeted at finding and extracting data about human genetic diseases and polymorphisms. Curators...

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Bibliographic Details
Published in:International journal of medical informatics (Shannon, Ireland) Ireland), 2005-03, Vol.74 (2), p.317-324
Main Authors: Dobrokhotov, Pavel B., Goutte, Cyril, Veuthey, Anne-Lise, Gaussier, Eric
Format: Article
Language:English
Subjects:
Database annotation
Databases, Protein
Document filtering
Genetic Diseases, Inborn - genetics
Humans
Polymorphism, Genetic
Statistical classifiers
Statistics as Topic
Swiss-Prot
Text mining
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Summary:Bio-medical knowledge bases are valuable resources for the research community. Original scientific publications are the main source used to annotate them. Medical annotation in Swiss-Prot is specifically targeted at finding and extracting data about human genetic diseases and polymorphisms. Curators have to scan through hundreds of publications to select the relevant ones. This workload can be greatly reduced by using bio-text mining techniques. Using a combination of natural language processing (NLP) techniques and statistical classifiers, we achieve recall points of up to 84% on the potentially interesting documents and a precision of more than 96% in detecting irrelevant documents. Careful analysis of the document pre-processing chain allows us to measure the impact of some steps on the overall result, as well as test different classifier configurations. The best combination was used to create a prototype of a search and classification tool that is currently tested by the database curators.
ISSN:1386-5056
1872-8243
DOI:10.1016/j.ijmedinf.2004.04.017