Comparative Evaluation of Machine Learning Techniques for Classifying DNA Sequences
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Comparative Evaluation of Machine Learning Techniques for Classifying DNA Sequences
Dr. K. Satyam1, Pamula DeviPriya2
1Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra
Pradesh, India.
2 Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra
Pradesh, India.
Abstract:In contemporary bioinformatics, DNA sequence categorisation is essential for identifying functional areas inside genomes and differentiating between various genetic patterns. Applications including disease detection, gene prediction, evolutionary research, and personalised therapy depend on accurate DNA sequence classification. Conventional biological analysis techniques are time-consuming and less scalable for big genomic datasets because they frequently call for substantial manual labour and domain knowledge. This paper suggests a machine learning-based method for effective and automated DNA sequence classification in order to overcome these difficulties. In this work, appropriate encoding techniques are used to convert DNA sequences into numerical representations that areacceptable for supervised learning systems. Several machine learning models are trained and assessed to ascertain theirclassification performance following preprocessing and feature extraction. Standard assessment criteria like accuracy, precision, recall, F1-score, and confusion matrix analysis are used to evaluate the models. The outcomes of experiments show that machine learning algorithms are capable of accurately classifying data and capturing underlying genetic patterns. The suggested method offers a framework for genomic sequence analysis that is both scalable and computationally effective. This work advances intelligent bioinformatics tools that enable quicker and more accurate genetic analysis by utilising machine learning techniques.
Keywords:DNA Sequence Classification, Machine Learning, Bioinformatics, Genomic Analysis, Supervised Learning, FeatureExtraction, Sequence Encoding, Classification Algorithms
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