• The korean journal of orthodontics
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• v.34 no.1 s.102
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• pp.23-31
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• 2004

As a research method that weighs and combines evidence, meta analysis produces evidence that is more powerful than the original studies. The purpose of this study was to compare the jntercanine/intermolar expansion and rapid/slow expansion in the maxillary expansion treatment using meta analysis. Medline was searched from 1979 to 2000 for all studies examining the stability of transverse expansion of the human maxilla, and 388 articles were found. Then these articles were reduced to 7 based on the defined inclusion and exclusion criteria, and a cumulative Meta evaluation score was computed for each study. The results were as follows; 1. The mean expansion in intermolar width was 6.0mm. of the 6.0mm, 4.8mm was retained and $20.0\%(1.2mm)$ was relapsed while wearing retainers. 2. In intercanines width, the mean expansion was 3.7mm. of the 3.7mm, 2.6mm was retained and $29.7\%(1.1mm)$ was relapsed while wearing retainers. 3. The differences In the amount of expansion and relapse between rapid expansion group and slow expansion group were less than $6\%(0.1-0.3mm)$. But, there might be differences in the skeletal/dental exapnsion ratios according to the expansion method.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.375-382
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• 2008

A protein's 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 WordNet thesaurus. In the first step, we constructed syntactic dependency paths from each protein to its location candidate, and then converted the syntactic dependency paths into dependency trees. In the second step, we retrieved root information of the syntactic dependency trees. In the final step, we extracted syn-semantic patterns of protein subtrees and location subtrees. From the root and subtree nodes, we extracted 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 from the training data, we extracted (protein, localization) pairs from the test sentences. Even with no biomolecular knowledge, our method showed reasonable performance in experimental results using Medline abstract data. 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%.