title: Transfer learning across ontologies for phenome-genome association prediction.
abstract: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.
  
  
  
  In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.
  
  
  
  Source code is available at http://compbio.cs.umn.edu/onto phenome .
  
  
  
  kuang@cs.umn.com.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.
authors: Raphael
authors: R
authors: Petegrosso
authors: Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors: Sunho
authors: S
authors: Park
authors: Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors: Tae Hyun
authors: TH
authors: Hwang
authors: Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.
authors: Rui
authors: R
authors: Kuang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D063990:Gene Ontology; D016678:Genome; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D060066:Protein Interaction Maps