Sokolov, Artem, authorBen-Hur, Asa, advisorAnderson, Chuck, committee memberMcConnell, Ross M., committee memberWang, Haonan, committee member2007-01-032007-01-032011http://hdl.handle.net/10217/52071With the growing number of sequenced genomes, automatic prediction of protein function is one of the central problems in computational biology. Traditional methods employ transfer of functional annotation on the basis of sequence or structural similarity and are unable to effectively deal with today's noisy high-throughput biological data. Most of the approaches based on machine learning, on the other hand, break the problem up into a collection of binary classification problems, effectively asking the question ''does this protein perform this particular function?''; such methods often produce a set of predictions that are inconsistent with each other. In this work, we present GOstruct, a structured-output framework that answers the question ''what function does this protein perform?'' in the context of hierarchical multilabel classification. We show that GOstruct is able to effectively deal with a large number of disparate data sources from multiple species. Our empirical results demonstrate that the framework achieves state-of-the-art accuracy in two of the recent challenges in automatic function prediction: Mousefunc and CAFA.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.machine learningprotein function predictionText