title: Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.
abstract: A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.
authors.affiliation: Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Yunlin 640, Taiwan. huangyf@yuntech.edu.tw
authors.forename: Yin-Fu
authors.initials: YF
authors.lastname: Huang
authors.forename: Chia-Ming
authors.initials: CM
authors.lastname: Wang
authors.forename: Sing-Wu
authors.initials: SW
authors.lastname: Liou
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D007438:Introns / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*