NDLI: Detection of Protein Subcellular Localization Based on a Full Syntactic Parser and Semantic Information

Content Provider	IEEE Xplore Digital Library
Author	Mi-Young Kim
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Sungshin Women's Univ., Seoul (Mi-Young Kim)
Abstract	A proteinpsilas subcellular localization is considered an essential part of the description of its associated biomolecular phenomena. As the volume of biomolecular reports has increased, there has been a great deal of research on text mining to detect protein subcellular localization information in documents. It has been argued that linguistic information, especially syntactic information, is useful for identifying the subcellular localizations of proteins of interest. However, previous systems for detecting protein subcellular localization information used only shallow syntactic parsers, and showed poor performance. Thus, there remains a need to use a full syntactic parser and to apply deep linguistic knowledge to the analysis of text for protein subcellular localization information. In addition, we have attempted to use semantic information from the WordNet thesaurus. To improve performance in detecting protein subcellular localization information, this paper proposes a three-step method based on a full syntactic dependency parser and semantic information. In the first step, we construct syntactic dependency paths from each protein to its location candidate. In the second step, we retrieve root information of the syntactic dependency paths. In the final step, we extract syn-semantic patterns of protein subtrees and location subtrees. From the root and subtree nodes, we extract syntactic category and syntactic direction as syntactic information, and synset offset of the WordNet thesaurus as semantic information. According to the root information and syn-semantic patterns of subtrees, we extract (protein, localization) pairs. Even with no biomolecular knowledge, our method shows reasonable performance in experimental results using Medline abstract data. In fact, our proposed method gave an F-measure of 74.53% for training data and 58.90% for test data, significantly outperforming previous methods, by 12-25%.
Starting Page	407
Ending Page	411
File Size	224148
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text mining Learning systems Protein engineering Information retrieval Thesauri Data mining Bioinformatics Information analysis Testing Fuzzy systems
Content Type	Text
Resource Type	Article

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