Abstract
Information based on DNA-sequence is significant for scientists to examine the functionalities of genes. Recently, there is a tremendous increase in biological-data generation. Hence it is essential to match and classify the DNA sequences. This study aims to perform effective approaches for pre-processing and feature extraction of DNA data. Here the feature extraction is performed using the count vectorizer. Besides, it intended to classify the DNA sequences using machine learning (ML) algorithms using k-mer function. The matched sequences are retrieved effectively via the use of a pattern-matching algorithm. The results revealed that the SVM linear classifier gives good result.
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Ravikumar, M., Prashanth, M.C., Guru, D.S. (2022). Matching Pattern in DNA Sequences Using Machine Learning Approach Based on K-Mer Function. In: Gunjan, V.K., Zurada, J.M. (eds) Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-96634-8_14
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DOI: https://doi.org/10.1007/978-3-030-96634-8_14
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