Genotyping of Single Nucleotide Polymorphisms Based on a Mathematical Model for Two-Dimensional Data
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- Satoh Kenichi
- Research Institute for Radiation Biology and Medicine, Hiroshima University
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- Ohtani Keiko
- Japan Biological Informatics Consortium
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- Ushijima Masaru
- Genome Center, Japanese Foundation for Cancer Research
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- Isomura Minoru
- Genome Center, Japanese Foundation for Cancer Research
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- Matsuura Masaaki
- Genome Center, Japanese Foundation for Cancer Research
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- Miki Yoshio
- Genome Center, Japanese Foundation for Cancer Research
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- Ohtaki Megu
- Research Institute for Radiation Biology and Medicine, Hiroshima University
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抄録
Classification methods typically applied to the Invader assay include k-means clustering and the normal mixture model for original two-dimensional data or angle data. Combining the normal mixture model and angle data might result in an inproved method. In fact, such an approach has the advantages that it can be used to evaluate the goodness of classification for each individual and angle data are easily handled. However, the method requires that the data have an origin, which implies that one cluster must be specified before clustering. Therefore, an alternative method using the normal mixture model is desirable. We propose a mathematical model with a latent time variable. Optimization is based mainly on a one-dimensional normal mixture model with two components, which provides stable computational results more quickly than can be obtained using a bivariate normal mixture model.
収録刊行物
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- 計量生物学
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計量生物学 25 (2), 61-67, 2004
日本計量生物学会
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詳細情報 詳細情報について
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- CRID
- 1390282679345104768
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- NII論文ID
- 10014334851
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- NII書誌ID
- AA11591618
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- ISSN
- 21856494
- 09184430
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- NDL書誌ID
- 7240861
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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
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可