PHENOstruct: Prediction of human phenotype ontology terms

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. This article is included in the Machine learning: life collection. sciences Asa Ben-Hur ( ) Corresponding author: [email protected] The authors declare that they have no competing interests. Competing interests: Kahanda I, Funk C, Verspoor K and Ben-Hur A. How to cite this article: PHENOstruct: Prediction of human phenotype ontology terms 2015, :259 (doi: ) using heterogeneous data sources [version 1; referees: 2 approved] F1000Research 4 10.12688/f1000research.6670.1 © 2015 Kahanda I . This is an open access article distributed under the terms of the , which Copyright: et al Creative Commons Attribution Licence permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This work was supported by the NSF Advances in Biological Informatics program through grants number 0965768 (awarded Grant information: to Dr. Ben-Hur) and 0965616 (originally awarded to Dr. Verspoor). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 16 Jul 2015, :259 (doi: ) First published: 4 10.12688/f1000research.6670.1 1 2 3,4