Abstract
BackgroundGenome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.ResultsThe domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves.ConclusionThis paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.
Highlights
Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown
Yeast PPI network data was obtained from DIP database [19]. 4,389 proteins and 14,338 protein-protein interactions were included in the network
The relationships between protein function similarity and domain context similarity in the PPI network were investigated based on the measurement indices
Summary
Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale proteinprotein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Highthroughput technologies, such as yeast-two hybrid, have provided large scale protein-protein interaction data, making it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks [1,2]. Direct methods are based on the assumption that interacting proteins probably have identical or similar functions [4,5,6,7]. This assumption is supported by previous studies which show that 70%-80% of proteins share at least one (page number not for citation purposes)
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