title: PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.
abstract: The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA.
authors.forename: Indika
authors.initials: I
authors.lastname: Kahanda
authors.affiliation: Computational Bioscience Program, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Funk
authors.affiliation: Department of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3010, Australia; Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Victoria, 3010, Australia.
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA.
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
keywords: human phenotype ontology; structured SVM
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