![]() ![]() The available executions are frequently best for a certain task. Additionally, the categorization of biological sequences was one of several tasks in which the convolutional neural network (CNN) performed well. The suggested approach may be used as a conduit element to provide a feeling of raw data that focuses on tiny sets of dimensions and reduces entropy. The suggested methods may be used with additional data kinds, such as larger datasets. The suggested method can therefore be used with any genome or DNA sequence, it has been discovered. ![]() It is important for both tumor suppression and carcinogenesis. Additionally, DNA methylation is a genetic alteration important for controlling how the genome functions. ![]() The base sequence of DNA transmits the knowledge that a cell needs to assemble RNA and protein components. In addition, the sequencing of DNA is a technique used to identify the exact nucleotide sequences in a DNA molecule. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.ĭNA is a kind of molecule that contains the genetic information needed by an organism to develop, survive, and reproduce. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. The results show that as the pattern length increases, the execution time of each algorithm varies. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. ![]()
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