title: Recognition of alternatively spliced cassette exons based on a hybrid model.
abstract: Alternative splicing (AS) is an important mechanism of gene regulation that contributes to protein diversity. It is of great significance to recognize different kinds of AS accurately so as to understand the mechanism of gene regulation. Many in silico methods have been applied to detecting AS with vast features, but the result is far from satisfactory. In this paper, we used the features proven to be useful in recognizing AS in previous literature and proposed a hybrid method combining Gene Expression Programming (GEP) and Random Forests (RF) to classify the constitutive exons and cassette exons which is the most common AS phenomenon. GEP will firstly make prediction to the samples of strong signal, and the other samples of weak signal will be distinguished with a more complex classifier based on RF. The experiment result indicates that this method can highly improve the recognition level in this issue.
authors: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China.
authors: Xiaokang
authors: X
authors: Zhang
authors: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: qkpeng@mail.xjtu.edu.cn.
authors: Qinke
authors: Q
authors: Peng
authors: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China.
authors: Liang
authors: L
authors: Li
authors: Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China.
authors: Xintong
authors: X
authors: Li
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics*; D001483:Base Sequence; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D005091:Exons / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA
keywords: Cassette exons; Constitutive exons; Gene expression programming; Hybrid model; Random forests