title: A support vector machine based method to predict success for polymerase chain reactions.
abstract: Polymerase chain reaction (PCR) is one of the most popular molecular biological techniques and has been widely applied in many areas. However, PCR still faces challenges nowadays. During recent decades, the experimental procedure of PCR, including the primer design, was always the focus of attention, while little attention was paid to the analysis of the PCR template, and still nobody can accurately predict whether or not a DNA sequence can be simply amplified using conventional Taq DNA polymerase-based PCR protocol. In this study, we focus on the DNA template, the subject of PCR experiment, and introduce a support vector machine (SVM) based method to help evaluate PCR result. Through the Jackknife cross-validation test, our method achieves an accuracy of 92.06%, with 93.62% sensitivity and 90.53% specificity.
authors.affiliation: Department of Mathematics, Bohai University, Jinzhou 121013, P.R. China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Yu
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.forename: Liangyu
authors.initials: L
authors.lastname: Meng
authors.forename: Chun
authors.initials: C
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D004247:DNA / Q000235:genetics*; D017931:DNA Primers / Q000235:genetics; D006801:Humans; D016133:Polymerase Chain Reaction / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D017931:DNA Primers; D004247:DNA