Assisting medical annotation in Swiss-Prot using statistical classifiers
Pavel Dobrokhotov, Cyril Goutte, Anne-Lise Veuthey, Eric Gaussier
Bio-medical knowledge bases are valuable resources for the research community. Original 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 one. This workload can be greatly reduced by using bio-text mining techniques. Using a combination of Natural Language Processing (NLP) techniques and Statistical Classigiers, we achieve recall points of up to 84% on the potentially interesting documents and a precision of more than 96% in detecting irrelevant document. 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. This article is an extended version of the papers presented at MIE 2003 and ISMB 2003.
International Journal of Medical Informatics 74(2-4):317-324.
2004_021.pdf (246.29 kB)