A simple and practical dictionary-based approach for identification of proteins in Medline abstracts.
OBJECTIVE
The aim of this study was to develop a practical and efficient protein identification system for biomedical corpora.


METHODS
The developed system, called ProtScan, utilizes a carefully constructed dictionary of mammalian proteins in conjunction with a specialized tokenization algorithm to identify and tag protein name occurrences in biomedical texts and also takes advantage of Medline "Name-of-Substance" (NOS) annotation. The dictionaries for ProtScan were constructed in a semi-automatic way from various public-domain sequence databases followed by an intensive expert curation step.


METHODS
The recall and precision of the system have been determined using 1000 randomly selected and hand-tagged Medline abstracts.


RESULTS
The developed system is capable of identifying protein occurrences in Medline abstracts with a 98% precision and 88% recall. It was also found to be capable of processing approximately 300 abstracts per second. Without utilization of NOS annotation, precision and recall were found to be 98.5% and 84%, respectively.


CONCLUSIONS
The developed system appears to be well suited for protein-based Medline indexing and can help to improve biomedical information retrieval. Further approaches to ProtScan's recall improvement also are discussed.
Egorov
Sergei
S
Ariadne Genomics, Inc, Rockville, MD 20850, USA.
Yuryev
Anton
A
Daraselia
Nikolai
N
D000043:Abstracting and Indexing; D000465:Algorithms; D000818:Animals; D004014:Dictionaries as Topic; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D008322:Mammals; D011506:Proteins; D009626:Terminology as Topic
D011506:Proteins