ppiPre (version 1.7)

SVMPredict: Predict false interactions using a training set

Description

Using a training set to train the SVM classifier, and predict false interactions in a user given PPI network.

Usage

SVMPredict(training_set,predict_set,output="falsePPIs-ppiPre.csv",organism="yeast",
           drop ="IEA", replaceNA=0)

Arguments

training_set

CSV format golden standard training set

predict_set

PPI network to do the prediction

output

CSV format file to ave the result

organism

One of "anopheles", "arabidopsis", "bovine", "canine", "chicken", "chimp", "ecolik12", "ecsakai", "fly", "human", "malaria", "mouse", "pig", "rat", "rhesus", "worm", "xenopus", "yeast" and "zebrafish."

drop

A set of evidence codes based on which certain annotations are dropped. Use NULL to keep all GO annotations.

replaceNA

The value to replace NA in training and predict set.

See Also

TopologicSims GOKEGGSims ComputeAllEvidences

Examples

#edges <- data.frame(node1=c("1132", "1133", "1134", "1134", "1145", "1147"),
  #                    node2=c("1134", "1134", "1145", "1147", "1147", "1149"),
  #                    label=c(1, 1, 1, 0, 0, 0))
  #graph<-graph.data.frame(edges,directed=FALSE)
  #trainsample <- "ppiPre-SVMPredict-trainsample.csv"
  #write.csv(edges,file=trainsample,row.names=FALSE) 
  #edges <- data.frame(node1=c("1132", "1133", "1134", "1134", "1146", "1147"),
  #                    node2=c("1133", "1134", "1142", "1147", "1147", "1149"),
  #                    label=c(1, 0, 1, 0, 1, 0))
  #graph<-igraph::graph.data.frame(edges,directed=FALSE)
  #predictsample <- "ppiPre-SVMPredict-predictsample.csv"
  #write.csv(edges,file=predictsample,row.names=FALSE) 
  #SVMPredict(trainsample, predictsample, organism="human", replaceNA=0)

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