Abstract
Suppose that we wish to know a group of proteins responsible for a certain cellular biological process. Here we propose to infer such a protein complex from a protein-protein interaction network by using a class of algorithm, which has originally been developed to achieve web page ranking that reflects user’s personal interest or context. The inference of proteins responsible for a given biological process, namely, personalized ranking of proteins is whereby performed in analogy with personalized ranking of web pages. Searching for the best approach to personalized protein ranking, we carry out a series of experiments to compare the performance between two major personalized ranking methods: the personalized PageRank algorithm and the continuous-attractor dynamics algorithm, both applied to a yeast protein-protein interaction network. Results of these comparison experiments suggest that the continuous-attractor dynamics algorithm is the most efficient for personalized protein ranking.
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Okamoto, H. (2012). Extracting Tailored Protein Complexes from Protein-Protein Interaction Networks. In: Lones, M.A., Smith, S.L., Teichmann, S., Naef, F., Walker, J.A., Trefzer, M.A. (eds) Information Processign in Cells and Tissues. IPCAT 2012. Lecture Notes in Computer Science, vol 7223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28792-3_30
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DOI: https://doi.org/10.1007/978-3-642-28792-3_30
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