Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study

. 2018 Feb 8;17:1176935118755354. doi: 10.1177/1176935118755354

Algorithm 1: THE SKELETON OF HOW THE LASSO ESTIMATE WORKS WHEN EMBEDDED WITH AIC SCORE FUNCTION AND FEATURE SELECTION RANKING METHOD WHILE THE SEARCH SPACE IS RESTRICTED BY MAPK-KEGG SIGNALING PATHWAY.
for i = 1 to length(Genes) do Y = GENE[i] Features=MAPK.kegg.prior(Y, GENES[–i]) PR = OrderFeatures(Y, fiiter.rank(Features)) for j = 1 to length(PR) do SP = Seareh,SpaceFromLassoPath(Y, PR) return BestFeatures = mini[(AIC(SP))] return FinalError = LOOCV(BestFeatures) PR= PR[,–j] end for return BestFeatures(Y, min(FinalError)) end for

Algorithm 1: THE SKELETON OF HOW THE LASSO ESTIMATE WORKS WHEN EMBEDDED WITH AIC SCORE FUNCTION AND FEATURE SELECTION RANKING METHOD WHILE THE SEARCH SPACE IS RESTRICTED BY MAPK-KEGG SIGNALING PATHWAY.

for i = 1 to length(Genes) do
Y = GENE[i]
Features=MAPK.kegg.prior(Y, GENES[–i])
PR = OrderFeatures(Y, fiiter.rank(Features))
for j = 1 to length(PR) do
SP = Seareh,SpaceFromLassoPath(Y, PR)
return BestFeatures = mini[(AIC(SP))]
return FinalError = LOOCV(BestFeatures)
PR= PR[,–j]
end for
return BestFeatures(Y, min(FinalError))
end for