2022
DOI: 10.1007/978-3-030-96634-8_14
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Matching Pattern in DNA Sequences Using Machine Learning Approach Based on K-Mer Function

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Cited by 2 publications
(1 citation statement)
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“…The rationale behind this decision was to sample some of the genes that we had worked on in our previous research endeavors [15], Our objective was to integrate automated learning algorithms and pattern-matching algorithms that are based on specific DNA sequences, in order to create a biological data collection that could be utilized in a classification process. We conducted experiments on a dataset that included DNA sequences, where we compared the effectiveness of searching for a specific pattern with other classification models, such as Random Forest [3,16], KNN [16][17][18][19][20], Naïve Bayes [21][22][23][24], Decision tree [23,[25][26][27][28][29][30], and Support Vector Machine [18,[31][32][33][34][35][36] with Linear [37,38], RBF [37,39], and sigmoid [21,40] classifiers, the results of these classifiers models are calculated by F1 score, recall, precision rate, execution time, and with the accuracy which calculates the most effective patternmatching classifier. The comparison of DNA sequences is a crucial task in various fields of research, including molecular biology and genetics.…”
Section: Methodology For Pm From Dna Sequencesmentioning
confidence: 99%
“…The rationale behind this decision was to sample some of the genes that we had worked on in our previous research endeavors [15], Our objective was to integrate automated learning algorithms and pattern-matching algorithms that are based on specific DNA sequences, in order to create a biological data collection that could be utilized in a classification process. We conducted experiments on a dataset that included DNA sequences, where we compared the effectiveness of searching for a specific pattern with other classification models, such as Random Forest [3,16], KNN [16][17][18][19][20], Naïve Bayes [21][22][23][24], Decision tree [23,[25][26][27][28][29][30], and Support Vector Machine [18,[31][32][33][34][35][36] with Linear [37,38], RBF [37,39], and sigmoid [21,40] classifiers, the results of these classifiers models are calculated by F1 score, recall, precision rate, execution time, and with the accuracy which calculates the most effective patternmatching classifier. The comparison of DNA sequences is a crucial task in various fields of research, including molecular biology and genetics.…”
Section: Methodology For Pm From Dna Sequencesmentioning
confidence: 99%