Developing Text Mining Based Algorithms for Classifying Biological Sequences(Text Mining I)
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概要
- 論文の詳細を見る
The paper focuses on developing the algorithms for discovering the frequent motifs and the ordered cooccurrence set of frequent motifs supporting the classification of the family of biological sequences. AprioriBioSequence is the name of our proposed algorithm, which has been developed from the algorithms of discovering the frequent patterns in document sentences of text mining. AprioriBioSequence can discover the frequent motifs without specifying the length of discovered motifs. Besides, paper also deals with the algorithm for discovering the ordered set of the co-occurrence frequent motifs for classifying the biological sequences. The experimental results of the proposed, algorithms with the E-Coli Promoter sequences are presented.
- 一般社団法人情報処理学会の論文
- 2004-12-04
著者
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Kiem Hoang
Center For Information Technology Vietnam National University
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PHUC DO
Center for Information Technology Vietnam National University, HCM city
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Phuc Do
Center For Information Technology Vietnam National University
関連論文
- Learning Transfer Rules from Annotated English-Vietnamese Bilingual Corpus(Text Mining I)
- Learning Transfer Rules from Annotated English-Vietnamese Bilingual Corpus
- Developing Text Mining Based Algorithms for Classifying Biological Sequences(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- Developing Text Mining Based Algorithms for Classifying Biological Sequences(Text Mining I)
- Learning Transfer Rules from Annotated English-Vietnamese Bilingual Corpus(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)