title: DFA7, a new method to distinguish between intron-containing and intronless genes.
abstract: Intron-containing and intronless genes have different biological properties and statistical characteristics. Here we propose a new computational method to distinguish between intron-containing and intronless gene sequences. Seven feature parameters α, β, γ, λ, θ, φ and σ based on detrended fluctuation analysis (DFA) are fully used, and thus we can compute a 7-dimensional feature vector for any given gene sequence to be discriminated. Furthermore, support vector machine (SVM) classifier with Gaussian radial basis kernel function is performed on this feature space to classify the genes into intron-containing and intronless. We investigate the performance of the proposed method in comparison with other state-of-the-art algorithms on biological datasets. The experimental results show that our new method significantly improves the accuracy over those existing techniques.
authors.affiliation: Mind-Brain Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
authors.forename: Chenglong
authors.initials: C
authors.lastname: Yu
authors.affiliation: Quant Investment Department, Huashang Fund Management Co., Ltd., Beijing, China.
authors.forename: Mo
authors.initials: M
authors.lastname: Deng
authors.affiliation: Electrical and Computer Engineering and CyLab Mobility Research Center, Carnegie Mellon University, Moffett Field, California, United States of America.
authors.forename: Lu
authors.initials: L
authors.lastname: Zheng
authors.affiliation: Department of Biological Sciences, Chicago State University, Chicago, Illinois, United States of America.
authors.forename: Rong Lucy
authors.initials: RL
authors.lastname: He
authors.affiliation: Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, Illinois,United States of America.
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Mathematical Sciences, Tsinghua University, Beijing, China.
authors.forename: Stephen S-T
authors.initials: SS
authors.lastname: Yau
mesh_terms: D001105:Archaea / Q000235:genetics; D001419:Bacteria / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D056890:Eukaryota / Q000235:genetics; D007438:Introns / Q000235:genetics*; D060388:Support Vector Machine