title: AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.
abstract: BACKGROUND
  Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model.
  
  
  RESULTS
  We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training.
  
  
  METHODS
  The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank .
  
  
  BACKGROUND
  gribskov@purdue.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
authors.forename: Biaobin
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Mathematics, Purdue University, West Lafayette, IN, USA.
authors.forename: Kyle
authors.initials: K
authors.lastname: Kloster
authors.affiliation: Department of Computer Science, Purdue University, West Lafayette, IN, USA.
authors.forename: David F
authors.initials: DF
authors.lastname: Gleich
authors.affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Gribskov
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004330:Drosophila / Q000378:metabolism; D063990:Gene Ontology; D006801:Humans; D011506:Proteins / Q000378:metabolism* / Q000502:physiology; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins