Bottom - Index of papers - Paper in HTML - Abstract - CUBIC
| Title: | Protein names precisely peeled off free text |
| Author: | Sven Mika and Burkhard Rost |
| Quote: |
Motivation: Automatically identifying protein names from the scientific literature is a prerequisite for the increasing demand for data-mining tools of 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 MEDLINE abstracts.
Results: Our new tagging-system NLProt is able to extract 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 utilising the local context of protein names in scientific literature. We challenge that our system may constitute the most general and precise method for tagging protein names.
Availability: http://cubic.bioc.columbia.edu/services/nlprot/
Contact: mika@cubic.bioc.columbia.edu