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... many domains, e.g. gene co-expression networks (Stuart et al., 2003; Carter et al., 2004; Butte and Kohane, 2000), protein-protein interaction networks (Jeong et al., 2001; Rzhetsky and Gomez, 2001; =-=Yook et al., 2004-=-), cell-cell interaction networks (Hartwell et al., 1999), the world wide web and social interaction networks (Barabasi and Bonabeau, 2003; Csanyi and Szendroi, 2004). In many real networks, the proba...

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... between the proteins. Computational analyses of these networks has already yielded valuable insights: the scale-free character of these networks and the disproportionate importance of \hub" proteins =-=[30]-=-; the combination of these networks with gene expression data to discern some of the dynamic character of the cell [8]; the use of PPI networks for inferring biological function [20], etc. As more PPI...

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...ive interactions. However, because of the assumed self-similarity features of SF networks, it has been claimed that if the real PPI network is SF, then any appropriately sampled subnetwork is also SF =-=[8]-=-. Thus, we might still gain valuable information by examining whether the publicly available PPI network data possess a power law node degree distribution characteristic of SF networks. A finite seque...

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...nctional modules from such interaction networks, for the purposes of understanding the behavior of organisms, protein function prediction and drug design is challenging and an active area of research =-=[7, 19, 38, 39, 34]-=-. The challenges involved are manifold. First, is the issue of data quality. Different experimental and insilico methods can be used to compute interactions, each with its own strengths and weaknesses...

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...), and out-degree, Pout(k), have tails of the form (3), with 2 < γ < 3. Similar studies on the degree distributions of protein interaction networks in various organisms have also been carried out. In =-=[200]-=-, the protein interaction network of S. cerevisiae was analysed using data from four different sources. As is often the case with data of this nature, there was little overlap between the interactions...

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...s that an initial seed neighborhood comprised of two cell cycle related hub proteins leads to far better results than using a single protein as input. But as Figure 4 indicates, this is only true for protein pairs that have high topological overlap. Note that pairs of proteins resulting in neighborhoods with high percentages of cell cycle related proteins are comprised of proteins with high topological overlap measure. 3.3 Neighborhood analysis for predicting essential yeast proteins Networks are a natural framework for understanding protein-protein interactions, see e.g. Jeong et al. (2001), Yook et al. (2004) and Deng et al. (2006). Knock-out experiments in lower organisms (e.g. yeast, fly, worm) have shown that essential proteins tend to be highly connected ‘hub’ proteins in protein-protein interaction networks (Jeong et al., 2001, 2003; Hahn and Kern, 2005). Here we Naive Non−Recursive 1 Initial Gene Recursive 1 Initial Gene Recursive 2 Initial Genes P er ce nt ag e of C el l C yc le R el at ed G en es 0 20 40 60 80 Percentage of Cell Cycle Related Genes , p−value= 1.6e−08 Fig. 3. Comparing the percentage of cell cycle proteins R (y-axis) in neighborhoods constructed in different ways for the Ye...

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...rved patterns in PPI networks. The framework proposed here can also be extended to include more general networks that capture the degree distribution of PPI networks more accurately, namely power-law =-=[33, 37]-=-, geometric [20], or exponential [8] degree distributions. The rest of this manuscript is organized as follows: In the next section, we first discuss graph models for PPI networks. We then analyze the...