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Computational representation of protein sequences for homology detection and classification

Oğul, Hasan
Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by probabilistic suffix trees. New sequence representations described in the thesis are applied on three challenging problems of computational biology: remote homology detection, subcellular localization prediction, and solvent accessibility prediction, with some problem-specific modifications. Rigorous experiments are conducted on common benchmarking datasets, and a comparative analysis is performed between the new methods and the existing ones for each problem. On remote homology detection tests, all three methods achieve competitive accuracies with the state-of-the-art methods, while being much more efficient. A combination of new representations are used to devise a hybrid system, called PredLOC, for predicting subcellular localization of proteins and it is tested on two distinct eukaryotic datasets. To the best of author̕s knowledge, the accuracy achieved by PredLOC is the highest one ever reported on those datasets. The maximal unique match method is resulted with only a slight improvement in solvent accessibility predictions.