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Where dr(v) is the number of directed edges pointed at vertex v. Then they defined a ”revitalized personalized PageRank”, rPPR, where: rPPR(v) = PageRank(v) (VI.5) dr (v) Bánky et al. (2013) applied this method to several different metabolic net- works, Mycobacterium tuberculosis, Plasmodium falciparum, and the MRSA Staphylococcus aureus SAA strain. By only looking at the rPPR, and not any of the known properties of the proteins aside from that, they were able to iden- tify many protein targets that are also known to have proven biological interest. The results gave higher scores to nodes with relatively high PageRanks com- pared to their degrees. This can identify nodes of high importance and may be promising new drug targets that are not hubs of the network. Using these results, they hope to be able to identify new important targets for further inves- tigation. With this method, Bánky et al. (2013) were confident that they would be able to use the PageRank algorithm to help find low-degree nodes with high intrinsic metabolic functionality. By using the personalized vector to factor out nodes with high degrees, Bánky et al. (2013) were able to find the non-hub nodes corresponding to essential reactions. 33PDF Image | MATHEMATICS BEHIND GOOGLE PAGERANK ALGORITHM
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