Nothing
GA_search_PPI <-
function(lambda,scaled_node_score,scaled_edge_score,PPI,
num_iter=1000, muCh=0.05, zToR=10, minsize=10){
## define the objective scoring function for condition specific subnetwork
all_genes<-names(scaled_node_score)
subset_score<-function(sub){
genes<-all_genes[sub==1]
n<-length(genes)
if(n<minsize){return(10000)}
else{
node_score<-sum(scaled_node_score[genes])/sqrt(n)
edges<- PPI[,1] %in% genes & PPI[,2] %in% genes
m<-sum(edges)
if(m==0)total_score<- -10000
if(m>0){
edge_score<-sum(scaled_edge_score[edges])/sqrt(m)
total_score<- lambda*edge_score + (1-lambda)*node_score
}
return (-total_score)
}
}
monitor <- function(obj) {
minEval = min(obj$evaluations);
filter = obj$evaluations == minEval;
# print(table(filter))
bestObjectCount = sum(rep(1, obj$popSize)[filter]);
if (bestObjectCount > 1) {
bestSolution = obj$population[filter,][1,];
} else {
bestSolution = obj$population[filter,];
}
outputBest = paste(obj$iter, " #selected=", sum(bestSolution),
" Best (Score=", -minEval, "):\n", sep="");
print(outputBest)
}
##Start search
gene_num <- length(scaled_node_score)
# num_lam <- length(lambda)
# num_gene_selected <- rep(0,num_lam)
# best_score <- rep(0,num_lam)
# optimal_subnet <- vector(length=num_lam,mode="list")
# GA_result <- vector(length=num_lam,mode="list")
# for(i in 1:num_lam){
print(paste("Working on lambda=",lambda))
GA_result <- rbga.bin(size=gene_num,evalFunc=subset_score,iters=num_iter,mutationChance=muCh,monitorFunc=monitor,zeroToOneRatio=zToR)
a <- which.min(GA_result$evaluations)
final <- GA_result$population[a,]
b <- which(final==1)
num_gene_selected <- length(b)
optimal_subnet <- b
best_score <- (-1)*min(GA_result$evaluations)
print(paste("Finished lambda=",lambda))
# }
return(list( Subnet_size = num_gene_selected, Best_Scores = best_score, Subnet = optimal_subnet, GA_obj = GA_result))
}
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