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Introduction: Polymerase Chain Reaction (PCR) has been a pivotal scientific technique since the twentieth century, and it is widely applied across various domains. Despite its ubiquity, challenges persist in efficiently amplifying specific DNA templates., Method: While PCR experimental procedures have garnered significant attention, the analysis of the DNA template, which is the experiment's focal point, has been notably overlooked. This study addresses the uncertainty surrounding the amplification of DNA fragments using conventional Taq DNA polymerase-based PCR protocols. The imperative need to characterize DNA templates and devise a reliable method for predicting PCR success is underscored., Result: In this study, we formulate a 72-dimensional feature vector representing a DNA template through the utilization of k-word order and modeling of physicochemical properties of double bases. Subsequently, a Support Vector Machine (SVM) model is employed to assess PCR results., Conclusion: A jackknife cross-validation test is used to evaluate the anticipated success rates, resulting in an overall accuracy of 95.77%. Sensitivity, specificity, and Matthew's Correlation Coefficient (MCC) stand at 95.75%, 95.79%, and 0.915, respectively., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Goal: Individual characteristics are determined through a genome consisting of a complex base combination. This base combination is reflected in the k-word profile, which represents the number of consecutive k bases. Therefore, it is important to analyze the genome-specific statistical specificity in the k-word profile to understand the characteristics of the genome. In this paper, we propose a new k-word-based method to analyze genome-specific properties. Methods: We define k-words consisting of the same number of bases as statistically identical k-words. The statistically identical k-words are estimated to appear at a similar frequency by statistical prediction. However, this may not be true in the genome because it is not a random list of bases. The ratio between frequencies of two statistically identical k-words can then be used to investigate the statistical specificity of the genome reflected in the k-word profile. In order to find important ratios representing genomic characteristics, a reference value is calculated that results in a minimum error when classifying data by ratio alone. Finally, we propose a genetic algorithm-based search algorithm to select a minimum set of ratios useful for classification. Results: The proposed method was applied to the full-length sequence of microorganisms for pathogenicity classification. The classification accuracy of the proposed algorithm was similar to that of conventional methods while using only a few features. Conclusions: We proposed a new method to investigate the genome-specific statistical specificity in the k-word profile which can be applied to find important properties of the genome and classify genome sequences.