Introduction

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.

Publications

  1. PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.
    Cite this
    Kahanda I, Funk C, Verspoor K, Ben-Hur A, 2015-01-01 - F1000Research

Credits

  1. Indika Kahanda
    Developer

    Department of Computer Science, Colorado State University, United States of America

  2. Christopher Funk
    Developer

    Computational Bioscience Program, University of Colorado School of Medicine, United States of America

  3. Karin Verspoor
    Developer

    Department of Computing and Information Systems, University of Melbourne, Australia

  4. Asa Ben-Hur
    Investigator

    Department of Computer Science, Colorado State University, United States of America

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Summary
AccessionBT001902
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByAsa Ben-Hur