Abstract
Instead of putting too much focus on current approaches to protein secondary structure pre-diction, the authors will look at the natural instincts of protein secondary structures, and pro-pose a schema representation which are offered for identifying regular patterns among various types of secondary protein structures. The schemas employ genetic algorithms base on a steady-state strategy and two disjunctive data sets will be used to verify fitness function for our approach. In this study, 904 schemas were found, and nearly half of the said schemas reached confidence of 70% and higher. Finally, the paper concludes with some illustrations of significant schemas produced as part of this study, with brief explanations of their significance.
Original language | English |
---|---|
Pages | 2104-2108 |
Number of pages | 5 |
State | Published - 17 Sep 2004 |
Event | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004 |
Conference
Conference | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings |
---|---|
Country | China |
City | Hangzhou |
Period | 15/06/04 → 19/06/04 |
Keywords
- Association rule mining
- Machine learning
- Pattern finding
- Secondary protein structures
- Steady-state genetic algorithm