PREDICTION OF GENEOME SEQUENCE USING MACHINE LEARNING ALGORITHMS
CARDIFF METROPOLITAN UNIVERSITY
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Machine learning is logically designed and invented to enable computers to assist humans in predicting the behaviour of systems and making sense of large, complex datasets. This technology uses experimental data to optimize clustering or classification of samples or features for making predictions for a target large complex unseen data sets. Therefore, the research contained in this practical research paper involves a full evaluation and analysis of different the machine learning algorithms and their various technical approaches used for the prediction for a certain given data set. Further discussed is both the optimisation of the algorithms and their application to datasets. The technical part of this paper is to design and implement different types of machine learning algorithm models which are ‘Decision tree’, ‘Multilayer perceptron’ and ‘Support vector machines’ by using the Python programming script to investigate and identify the best machine learning classifier in terms of accuracy for a given dataset of a bacterial genome sequencing data obtained from a data controller, to predict the next character in a DNA genome sequencing, and produce a very critical analysis and evaluations of the different machine learning algorithm approaches that were used and implemented during the experiments to compare between its results of accuracies.
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