biNetwalk: Bipartite Random Walk

In abhik1368/netpredictor: Algorithms to search for missing link in a graph.

Description

Randomm walk with restart on Bipartite networks

Usage

biNetwalk(g1, s1, s2, normalise = c("laplace", "none", "chen"),
  dataSeed = NULL, restart = 0.8, verbose = T, weight = FALSE)

Arguments

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.

References

• 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.

Examples

## 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)

abhik1368/netpredictor documentation built on May 10, 2019, 4:09 a.m.