Description Usage Arguments References Examples
Randomm walk with restart on Bipartite networks
1 2 |
g1 |
Bipartite graph igraph object. |
s1 |
Accepts a matrix object of similarity scores for targets. |
s2 |
Accepts a matrix object similarity scores for compounds. |
normalise |
Normalisation of matrix using laplacian, Chen, None(the transition matrix will be column normalized) |
dataSeed |
seeds file |
restart |
restart value |
weight |
if we want to use a weighted network . Options are either TRUE or FALSE. |
Chen X, et al. Drug target interaction prediction by random walk on the heterogeneous network. Mol. BioSyst 2012;8:1970-1978.
Vanunu O, Sharan R. Proceedings of the German Conference on Bioinformatics. Germany: GI; 2008. A propagation-based algorithm for inferring gene-disease assocations; pp. 54–63.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
data(Enzyme)
A <- enzyme_ADJ
S2 = enzyme_Csim
S1 = enzyme_Gsim
g1 = graph.incidence(A)
M3 <- biNetwalk(g1,s1=S1,s2=S2,normalise="laplace", dataSeed=NULL,restart=0.8,
parallel=FALSE, verbose=T,weight=FALSE)
dataF<- read.csv("seedFile.csv",header=FALSE)
Mat <- biNetwalk(g1,s1=S1,s2=S2,normalise="laplace", dataSeed=dataF,restart=0.8,
parallel=TRUE,verbose=T,weight=FALSE)
## End(Not run)
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