Using machine learning and data splitting methods to predict a genome sequence
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This body of work consists of the analysis of various machine learning techniques and their principles in operation. There is discussion of applications to datasets and examples of previous work where machine learning was used to analyse DNA sequences for prediction purposes. There is further discussion on the overlaying principles used in DNA sequencing itself and the practices that have emerged through trial and error and breakthroughs over the years. The technical purpose of this work shows the use of algorithms designed in Python programming language to normalise and sort a DNA sequence from the genome of Pseudomonas aeruginosa PAO1, an opportunistic bacterium. Results are derived from Python development of machine learning algorithms SVM, Decision Trees, Random Forest and Naïve Bayes to analyse the sorted and normalised datasets. Performance metrics detailing accuracy, precision and f1-score are then analysed and visualised using the pandas library in Python. Improvements and recommendations are detailed from the outcome of the experiments outlined in this body of work for use in further research in this area.
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