Protein names precisely peeled off free text.
BACKGROUND
Automatically identifying protein names from the scientific literature is a pre-requisite for the increasing demand in data-mining this wealth of information. Existing approaches are based on dictionaries, rules and machine-learning. Here, we introduced a novel system that combines a pre-processing dictionary- and rule-based filtering step with several separately trained support vector machines (SVMs) to identify protein names in the MEDLINE abstracts.


RESULTS
Our new tagging-system NLProt is capable of extracting protein names with a precision (accuracy) of 75% at a recall (coverage) of 76% after training on a corpus, which was used before by other groups and contains 200 annotated abstracts. For our estimate of sustained performance, we considered partially identified names as false positives. One important issue frequently ignored in the literature is the redundancy in evaluation sets. We suggested some guidelines for removing overly inadequate overlaps between training and testing sets. Applying these new guidelines, our program appeared to significantly out-perform other methods tagging protein names. NLProt was so successful due to the SVM-building blocks that succeeded in utilizing the local context of protein names in the scientific literature. We challenge that our system may constitute the most general and precise method for tagging protein names.


BACKGROUND
http://cubic.bioc.columbia.edu/services/nlprot/
Mika
Sven
S
CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. mika@cubic.bioc.columbia.edu
Rost
Burkhard
B
D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D004014:Dictionaries as Topic; D004015:Dictionaries, Chemical as Topic; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D010363:Pattern Recognition, Automated / Q000379:methods; D011506:Proteins / Q000145:classification*; D009626:Terminology as Topic
D011506:Proteins