CYNA to ischemic stroke, i.e., binding affinity also should be considered. The affinity measure is derived from chemical proteomics data PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19775295 directly and obtained by the following equation: at 1 pt;comp lnpt pt 2 Where pt and pt,comp represent a rough estimate of the amount of pulled-down protein before and after treatment by CYNA, respectively. Since in chemical proteomics the drug is always presented at a large excess of constant concentration, ln is used to down weigh parameter influX X at and at denote the sum of affinities for the CYNA’s targets and ence. Therefore, t2Ttar t2Ttar;net t2Ttar;net targets on the pathway sub-network and its periphery nodes, respectively. Hence, the last feature, P at P could be interpreted as the ratio of CYNA’s affinities on the pathway suba t t2Ttar network to overall affinity of CYNA’s targets used from comparative proteomic analysis. Network scoring anti-ischemic stroke of CYNA Scoring network effect of a group of seed nodes. In order to obtain CYNA’s effect to all the genes on the PPI network, we applied the HC030031 chemical information algorithm of random walk with restart, which is used in many areas, such as identifying of functional modules, modeling the evolution of social networks and so on. The algorithm can compute all the nodes’ score of the network based on a group of seed nodes. In this paper, we used a weighted PPI network as the network and ischemic stroke associated genes or protein targets of CYNA as the seed nodes. The algorithm could be described as follows:A seed node is chosen from the seed set S before the random walk starting. At each step, the random walker either moves to a chosen neighbor u2N of the current node v, randomly, or it restarts at one of the nodes in the seed set S. The probability of restarting at a given time step is a fixed parameter, which is denoted by r. For each restart, the probability of restarting at v2S suggests PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19777101 the degree of association between v and the seed set S. For each move, the probability of moving to interacting partner u of the current node v is proportional to the reliability of the interaction between u and v. This process could be represented as 6 / 15 Cynandione A’s Anti-Ischemic Stroke Effects follows: xt1 1 rPxt rx0 3 where P is the adjacency matrix of the weighted PPI network, representing the coupling strength of nodes in the network; r 2 is a parameter denoting the restart probability which needs to be calibrated with real data; xt is a vector in which xt denotes the probability that the node will be at node v at time t; x0 is a vector representing the strength of seed nodes. After a sufficiently long time, the probability of being at node v at a random time step provides a measure of the functional association between v and the genes in seed set S, hence, the effect strength of seed set S to each nodes in the network is defined by steady-state probability vector x1 when xt+1 = xt. Scoring ischemic stroke’s effect on the human PPI network. Taking ischemic stroke associated genes as the seed nodes. Although it can be assumed that the initial strength values x0 of different seed nodes are different as the associated degree of different ischemic stroke genes to ischemic stroke is varying, for simplicity, all ischemic stroke associated genes are treated equally in this algorithm, and initial vector x0 could thus be defined as x0 = 1 if v is a seed otherwise x0 = 0. Then ischemic stroke effect score of each node in the human network was computed by random walk with re