2014年
Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based on Frequency and Correlation Coefficient
ADVANCES IN SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION
- 巻
- 295
- 号
- 開始ページ
- 89
- 終了ページ
- 98
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-319-07695-9_8
- 出版者・発行元
- SPRINGER-VERLAG BERLIN
Next-generation sequencing techniques produce an enormous amount of sequence data. Analyzing these sequences requires an efficient method that can handle large amounts of data. Self-organizing maps (SOMs), which use the frequencies of N-tuples, can categorize sets of DNA sequences with unsupervised learning. In this study, SOM using correlation coefficients among nucleotides was proposed, and its performance was examined in the experiments through mapping experiments of the genome sequences of several species and classification experiments using Pareto learning SOMs.
- リンク情報
- ID情報
-
- DOI : 10.1007/978-3-319-07695-9_8
- ISSN : 2194-5357
- Web of Science ID : WOS:000391155400008