Playing biology's name game: identifying protein names in scientific text.
A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.
Hanisch
Daniel
D
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. Daniel.Hanisch@scai.fhg.de
Fluck
Juliane
J
Mevissen
Heinz-Theodor
HT
Zimmer
Ralf
R
D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D030541:Databases, Genetic; D004014:Dictionaries as Topic; D015894:Genome, Human; D006801:Humans; D016239:MEDLINE; D015233:Models, Statistical; D011506:Proteins; D009626:Terminology as Topic
D011506:Proteins