Statistical Comparative Study of Multiple Sequence Alignment Scores of Iterative Refinement Algorithms
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- Wakatsu Daigo
- Information Engineering Course, Graduate School of Engineering and Science, University of the Ryukyus
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- Okazaki Takeo
- Department of Information Engineering, Faculty of Engineering, University of the Ryukyus
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抄録
Iterative refinement algorithm is a useful method to improve the alignment results. In this paper, we evaluated different iterative refinement algorithms statistically. There are four iterative refinement algorithms: remove first (RF), bestfirst (BF), random (RD), and tree-based (Tb) iterative refinement algorithm. And there are two scoring functions for measuring the iteration judgment step: log expectation (LE) and weighted sum-of-pairs (SP) scores. There are two sequence clustering methods: neighbor-joining (NJ) method and unweighted pair-group method with arithmetic mean (UPGMA). We performed comprehensive analyses of these alignment strategies and compared these strategies using BAliBASE SP (BSP) score. We observed the behavior of scores from the view point of cumulative frequency (CF) and other basic statistical parameters. Ultimately, we tested the statistical significance of all alignment results by using Friedman nonparametric analysis of variance (ANOVA) test for ranks and Scheffé multiple comparison test.
収録刊行物
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- IPSJ Transactions on Bioinformatics
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IPSJ Transactions on Bioinformatics 2 74-82, 2009
一般社団法人 情報処理学会
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詳細情報
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- CRID
- 1390282680271223040
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- NII論文ID
- 110007990342
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- NII書誌ID
- AA12177013
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- ISSN
- 18826679
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可