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Nonlinear Dynamics of Networks

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Determining Protein Function By Clustering Within Interaction Networks

Carl Kingsford

University of Maryland


Abstract:   Protein-protein interaction networks are an increasingly useful source of data from which to computationally predict protein functions. One approach to automated detection of protein complexes and prediction of involvement in biological processes is to divide an interaction network into biologically meaningful modules or clusters. I will present several graph clustering techniques and illustrate their usefulness for predicting protein annotations, including their ability to identify disease-related genes. In addition, I will describe a novel method to decompose a hierarchical tree decomposition into a collection of clusters that optimally match a set of known annotations. We find that our approach generally outperforms the commonly used heuristics. The techniques are general and may be useful in other applications network clustering is employed. This work is joint with Saket Navlakha, James White, Niranjan Nagarajan, and Mihai Pop.

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