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FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis


Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at



bimolecular interaction network database


Database of Interacting Proteins


edge clustering coefficient


highly connected subgraphs


Laplacian network partitioning correlations


molecular complex detection


Munich Information Center for Protein Sequences


non-negative matrix factorization


protein-protein interactions


restricted neighborhood search clustering algorithm


support vector machine


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Correspondence to Subhadip Basu.

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Saha, S., Chatterjee, P., Basu, S. et al. FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis. Cell Mol Biol Lett 19, 675–691 (2014).

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  • Protein interaction network
  • Protein function prediction
  • Functional groups
  • Neighborhood analysis
  • Relative functional similarity
  • Edge clustering coefficient