Abstract
A new method for E. coli DNA segment classification on promoters and non-promoters is presented. The algorithm is based on the Independent Component Analysis (ICA). Since the DNA segments are composed of discrete symbols, this paper contains two major steps: (1) Position-dependent transformation of DNA segments to real number sequences, and (2) Applications of the ICA to the E. coli promoter recognition. These steps are related to each other. Therefore, algorithmic explanations are given in detail while referring mutually. The automatic precision of 93.7% is obtained. Since the presented method allows threshold adjustments, twilight-zone data can be further cross-checked individually so that false negatives are reduced.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 |
| Pages | 686-691 |
| Number of pages | 6 |
| Publication status | Published - 2004 |
| Event | Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 - Stanford, CA Duration: 2004 Aug 16 → 2004 Aug 19 |
Other
| Other | Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 |
|---|---|
| City | Stanford, CA |
| Period | 04/8/16 → 04/8/19 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Promoter recognition for E. coli DNA segments by independent component analysis. / Matsuyama, Yasuo; Kawamura, Ryo.
Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004. 2004. p. 686-691.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Promoter recognition for E. coli DNA segments by independent component analysis
AU - Matsuyama, Yasuo
AU - Kawamura, Ryo
PY - 2004
Y1 - 2004
N2 - A new method for E. coli DNA segment classification on promoters and non-promoters is presented. The algorithm is based on the Independent Component Analysis (ICA). Since the DNA segments are composed of discrete symbols, this paper contains two major steps: (1) Position-dependent transformation of DNA segments to real number sequences, and (2) Applications of the ICA to the E. coli promoter recognition. These steps are related to each other. Therefore, algorithmic explanations are given in detail while referring mutually. The automatic precision of 93.7% is obtained. Since the presented method allows threshold adjustments, twilight-zone data can be further cross-checked individually so that false negatives are reduced.
AB - A new method for E. coli DNA segment classification on promoters and non-promoters is presented. The algorithm is based on the Independent Component Analysis (ICA). Since the DNA segments are composed of discrete symbols, this paper contains two major steps: (1) Position-dependent transformation of DNA segments to real number sequences, and (2) Applications of the ICA to the E. coli promoter recognition. These steps are related to each other. Therefore, algorithmic explanations are given in detail while referring mutually. The automatic precision of 93.7% is obtained. Since the presented method allows threshold adjustments, twilight-zone data can be further cross-checked individually so that false negatives are reduced.
UR - http://www.scopus.com/inward/record.url?scp=14044264215&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=14044264215&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:14044264215
SN - 0769521940
SP - 686
EP - 691
BT - Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
ER -