A Statistical Analysis and Machine Learning of Genomic Data

Machine learning enables a computer to learn a relationship between two assumingly related types of information. One type of information could thus be used to predict any lack of informaion in the other using the learned relationship. During the last decades, it has become cheaper to collect biological information, which has resulted in increasingly large amounts of data. Biological information such as DNA is currently analyzed by a variety of tools. Although machine learning has already been used in various projects, a flexible tool for analyzing generic biological challenges has not yet been made. The recent advancements in the DNA sequencing technologies (nextgeneration sequencing) decreased the time of sequencing a human genome from weeks to hours and the cost of sequencing a human genome from million dollars to a thousand dollars. Due to this drop in costs, a large amount of genomic data are produced. This thesis implemented the supervised and unsupervised machine learning algorithms for the genomic data. Distances are an integral part of all machine learning algorithms and hence play a central role in the analysis of most genomic data. The distance that is used for any particular task can have a profound effect on the output of the machine learning method and thus, it is essential that users ensure that the same distance method is used when comparing machine learning algorithms