net.perf: Link Prediction Performance
In abhik1368/netpredictor: Algorithms to search for missing link in a graph.

Description Usage Arguments Details Value Examples

This function samples links and removies links from the adjacency matrix and predicts them and calculates area under accumulation curve, AUC, bedroc, and Enrichment factor.

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net.perf(A, S1, S2, restart = 0.8, alpha = 0.5, lamda = 0.5,
  relinks = 100, numT = 2, norm = "laplace", Calgo = c("rwr", "nbi",
  "netcombo", "all"))

`A`	Drug target association matrix
`S1`	Sequence similarity matrix object
`S2`	Accepts a matrix object similarity scores for compounds.
`restart`	restart value if using rwr or netcombo
`alpha`	alpha value if using nbi or netcombo
`lamda`	lamda value if using nbi or netcombo
`relinks`	Number of links to remove randomly from the input matrix.
`numT`	Frequency of the number of targets.
`norm`	normalization of matrices options are "laplace" or "none".
`Calgo`	Algorithm to use for Bipartite link prediction options are "rwr","nbi" & "netcombo".

get the performance of the link Prediction algorithms.

it returns a list of aucc,auc, bedorc,enrichment factor and auc (top 10

Truchon et al. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem. J. Chem. Inf. Model. (2007) 47, 488-508.
Sheridan RP et al. Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J. Chem. Inf. Comput. Sci. (2001) 41, 1395-1406.

## Not run: 
data(Enzyme)
A = enzyme_ADJ
S1 = enzyme_Gsim
S2= enzyme_Csim
m = net.perf(A,S1,S2,alpha=0.5,lamda=0.5,relinks = 50,numT=2,norm="laplace",Calgo="nbi")

## End(Not run)