Identifying splicing sites in eukaryotic RNA: support vector machine approach.
We introduce a new method for splicing sites prediction based on the theory of support vector machines (SVM). The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In the process of splicing sites prediction, the statistical information of RNA secondary structure in the vicinity of splice sites, e.g. donor and acceptor sites, is introduced in order to compare recognition ratio of true positive and true negative. From the results of comparison, addition of structural information has brought no significant benefit for the recognition of splice sites and had even lowered the rate of recognition. Our results suggest that, through three cross validation, the SVM method can achieve a good performance for splice sites identification.
Sun
Ying-Fei
YF
Institute of Bioinformatics, State Key Laboratory of Intelligent Technology and System, Tsinghua University, Beijing 100084, People's Republic of China. syfei@mail.au.tsinghua.edu.cn
Fan
Xiao-Dan
XD
Li
Yan-Da
YD
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D005057:Eukaryotic Cells; D006801:Humans; D016571:Neural Networks, Computer; D012326:RNA Splicing; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA