Abstract

High-throughput experimental methods, such as yeast-two-hybrid and phage display, have fairly high levels of false positives (and false negatives). Thus the list of protein-protein interactions detected by such experiments would need additional wet laboratory validation. It would be useful if the list could be prioritized in some way. Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are reviewed in this paper, with a focus on techniques that rely only on topological information of the protein interaction network derived from such high-throughput experiments. In particular, we discuss indices that are abstract mathematical characterizations of networks of reliable protein-protein interactions—e.g., “interaction generality ” (IG), “interaction reliability by alternatve pathways” (IRAP), and “functional similarity weighting ” (FSWeight). We also present indices that are based on explicit motifs associated with true-positive protein interactions—e.g., “new interaction generality” (IG2) and “meso-scale motifs ” (NeMoFinder). Keywords: Protein-protein interaction network, graph mining, network motif. 1

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