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Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin
Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
This study investigates an application of active machine learning to a protein dataset developed to identify the source of mutations which give rise to mitochondrial disease. The dataset is labeled according to the protein's location of origin in the cell; whether in the mitochondria or not, or a specific target location in the mitochondria's outer or inner membrane, its matrix, or its ribosomes. This dataset forms a labeling hierarchy. A new machine learning approach is investigated to learn the high-level classifier, i.e., whether the protein is a mitochondrion, by separately learning finer-grained target compartment concepts and combining the results. This …