Role-similarity based functional prediction in networked systems: Application to the yeast proteome

Abstract

We propose a general method to predict functions of vertices where: 1. The wiring of the network is somehow related to the vertex functionality. 2. A fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme is tested on model networks were the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

References in corpus (7)

• Statistical mechanics of complex networks
• The structure and function of complex networks
• Lethality and centrality in protein networks
• Functional cartography of complex metabolic networks
• Specificity and stability in topology of protein networks
• Mean-field theory for scale-free random networks
• Predicting protein functions with message passing algorithms

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• Detecting degree symmetries in networks
• Structural patterns in complex systems using multidendrograms
• Signatures of currency vertices