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A Simple and Practical Dictionary-based Approach for Identification of Proteins in Medline Abstracts

Affiliation of the authors: Ariadne Genomics, Inc., Rockville, MD.

Correspondence and reprints: Nikolai Daraselia, PhD, Ariadne Genomics, Inc., 9700 Great Seneca Highway, Rockville, MD 20850; e-mail: <nikolai{at}ariadnegenomics.com>.

Received for publication: 09/08/03; accepted for publication: 01/11/04.

Objective: The aim of this study was to develop a practical and efficient protein identification system for biomedical corpora.

Design: 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.

Measurements: The recall and precision of the system have been determined using 1,000 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.

Conclusion: 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.

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