Prediction of success for polymerase chain reactions using the Markov maximal order model and support vector machine.
J Theor Biol
; 369: 51-8, 2015 Mar 21.
Article
en En
| MEDLINE
| ID: mdl-25636491
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Reacción en Cadena de la Polimerasa
/
Cadenas de Markov
/
Máquina de Vectores de Soporte
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Theor Biol
Año:
2015
Tipo del documento:
Article