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  used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernels. Only rarely has the hierarchy been used as a second layer of the network connecting the protein network with functional annotations . 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 flows on this two-layer  model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank uses an adaptive diffusion mechanism . 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  find that both BirgRank and AptRank outperform the previous methods, especially when only 10\% of the data are given for training. AptRank naturally combines protein-protein associations and  function-function relationships into a two-layer network model , and takes full advantage of the hierarchical structure of the Gene Ontology, using directional diffusion without flattening the ontological hierarchy into a similarity kernel. Introducing an adaptive mechanism to the traditional, fixed-parameter model of PageRank greatly improves the accuracy of protein function prediction.
Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood- 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 . 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 an application of traditional PageRank with fixed decay parameters. In contrast, AptRank uses an adaptive mechanism to improve the performance of BirgRank . We evaluate both methods in predicting protein function on yeast, fly, and human datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design three validation strategies: missing function prediction, de novo function prediction, and guided function prediction to comprehensively evaluate all six methods. We find that both BirgRank and AptRank outperform the others, especially in missing function prediction when using only 10\% of the data for training. AptRank combines protein-protein associations and the GO function-function hierarchy into a two-layer network model without flattening the hierarchy into a similarity kernel. Introducing an adaptive mechanism to the traditional, fixed-parameter model of PageRank greatly improves the accuracy of protein function prediction.
D
,
R
neighborhood-based
neighborhood-
A
either
R
kernels. Only rarely has the hierarchy been used as a second layer of the network connecting the protein network with functional annotations
kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model
D
flows
A
graph
R
a direct
an
R
diffusion mechanism
mechanism to improve the performance of BirgRank
R
the ability of both methods to predict
both methods in predicting
D
protein
A
design three validation strategies: missing function prediction, de novo function prediction, and guided function prediction to comprehensively evaluate all six methods. We
R
previous methods, especially when
others, especially in missing function prediction when using
D
are given
D
naturally
A
the GO
R
relationships
hierarchy
D
, and takes full advantage of the hierarchical structure of the Gene Ontology, using directional diffusion
D
ontological