Assessment of DNA Sequence Encoding Techniques for Machine Learning Algorithms Using a Universal Bacterial Marker

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Encoding Techniques for Machine Learning Algorithms Using a Universal Bacterial Marker</title> <meta name="description" content="Assessment of DNA Sequence Encoding Techniques for Machine Learning Algorithms Using a Universal Bacterial Marker"> <meta name="keywords" content="DNA encoding, machine learning, Fourier transform, Fourier transformation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" 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</div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Assessment of DNA Sequence Encoding Techniques for Machine Learning Algorithms Using a Universal Bacterial Marker</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Santiba%C3%B1ez%20Oyarce">Diego Santiba帽ez Oyarce</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernanda%20Bravo%20Cornejo"> Fernanda Bravo Cornejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Camilo%20Cerda%20Sarabia"> Camilo Cerda Sarabia</a>, <a href="https://publications.waset.org/abstracts/search?q=Bel%C3%A9n%20D%C3%ADaz%20D%C3%ADaz"> Bel茅n D铆az D铆az</a>, <a href="https://publications.waset.org/abstracts/search?q=Esteban%20G%C3%B3mez%20Ter%C3%A1n"> Esteban G贸mez Ter谩n</a>, <a href="https://publications.waset.org/abstracts/search?q=Hugo%20Osses%20Prado"> Hugo Osses Prado</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Caulier-Cisterna"> Ra煤l Caulier-Cisterna</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Vergara-Quezada"> Jorge Vergara-Quezada</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Moya-Beltr%C3%A1n"> Ana Moya-Beltr谩n</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies. <iframe src="https://publications.waset.org/abstracts/190369.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20encoding" title="DNA encoding">DNA encoding</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transform" title=" Fourier transform"> Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transformation" title=" Fourier transformation"> Fourier transformation</a> </p> <a href="https://publications.waset.org/abstracts/190369/assessment-of-dna-sequence-encoding-techniques-for-machine-learning-algorithms-using-a-universal-bacterial-marker" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">23</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Assessment of DNA Sequence Encoding Techniques for Machine Learning Algorithms Using a Universal Bacterial Marker