title: Prediction of success for polymerase chain reactions using the Markov maximal order model and support vector machine.
abstract: Polymerase chain reaction (PCR) is hailed as one of the monumental scientific techniques of the twentieth century, and has become a common and often indispensable technique in many areas. However, researchers still frequently find some DNA templates very hard to amplify with PCR, although many kinds of endeavors were introduced to optimize the amplification. In fact, during the past decades, the experimental procedure of PCR was always the focus of attention, while the analysis of a DNA template, the PCR experimental subject itself, was almost neglected. Up to now, nobody can certainly identify whether a fragment of DNA can be simply amplified using conventional Taq DNA polymerase-based PCR protocol. Characterizing a DNA template and then developing a reliable and efficient method to predict the success of PCR reactions is thus urgently needed. In this study, by means of the Markov maximal order model, we construct a 48-D feature vector to represent a DNA template. Support vector machine (SVM) is then employed to help evaluate PCR result. To examine the anticipated success rates of our predictor, jackknife cross-validation test is adopted. The overall accuracy of our approach arrives at 93.12%, with the sensitivity, specificity, and MCC of 94.68%, 91.58%, and 0.863%, respectively.
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China; Research Institute of Food Science, Bohai University, Jinzhou 121013, China. Electronic address: lichwun@163.com.
authors: Chun
authors: C
authors: Li
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors: Yan
authors: Y
authors: Yang
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors: Wenchao
authors: W
authors: Fei
authors: College of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China.
authors: Ping-an
authors: PA
authors: He
authors: Department of Applied Mathematics, Shanghai Institute of Technology, Shanghai 201418, China.
authors: Xiaoqing
authors: X
authors: Yu
authors: Research Institute of Food Science, Bohai University, Jinzhou 121013, China; College of Chemistry, Chemical Engineering and Food Safety, Bohai University, Jinzhou 121013, China.
authors: Defu
authors: D
authors: Zhang
authors: Research Institute of Food Science, Bohai University, Jinzhou 121013, China; College of Chemistry, Chemical Engineering and Food Safety, Bohai University, Jinzhou 121013, China.
authors: Shumin
authors: S
authors: Yi
authors: Research Institute of Food Science, Bohai University, Jinzhou 121013, China; College of Chemistry, Chemical Engineering and Food Safety, Bohai University, Jinzhou 121013, China.
authors: Xuepeng
authors: X
authors: Li
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors: Jin
authors: J
authors: Zhu
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors: Changzhong
authors: C
authors: Wang
authors: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors: Zhifu
authors: Z
authors: Wang
mesh_terms: D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D016133:Polymerase Chain Reaction / Q000379:methods*; D060388:Support Vector Machine
keywords: Amplicon; DNA template; Markov chains; Polymerase chain reactions; Support vector machines