title: Intron identification approaches based on weighted features and fuzzy decision trees.
abstract: Current computational predictions of splice sites largely depend on the sequence patterns of known intronic sequence features (ISFs) described in the classical intron definition model (IDM). The computation-oriented IDM (CO-IDM) clearly provides more specific and concrete information for describing intron flanks of splice sites (IFSSs). In the paper, we proposed a novel approach of fuzzy decision trees (FDTs) which utilize (1) weighted ISFs of twelve uni-frame patterns (UFPs) and forty-five multi-frame patterns (MFPs) and (2) gain ratios to improve the performances in identifying an intron. First, we fuzzified extracted features from genomic sequences using membership functions with an unsupervised self-organizing map (SOM) technique. Then, we brought in different viewpoints of globally weighting and crossly referring in generating fuzzy rules, which are interpretable and useful for biologists to verify whether a sequence is an intron or not. Finally, the experimental results revealed the effectiveness of the proposed method in improving the identification accuracy. Besides, we also implemented an on-line intronic identifier to infer an unknown genomic sequence.
authors.affiliation: Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road Section 3, Touliu, Yunlin, Taiwan 640, ROC. huangyf@yuntech.edu.tw
authors.forename: Yin-Fu
authors.initials: YF
authors.lastname: Huang
authors.forename: Ching-Ping
authors.initials: CP
authors.lastname: Liang
authors.forename: Sing-Wu
authors.initials: SW
authors.lastname: Liou
mesh_terms: D019295:Computational Biology; D003663:Decision Trees; D017143:Fuzzy Logic; D006801:Humans; D007438:Introns; D008957:Models, Genetic