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#' RPSSM feature
#' @description To obtain this feature, first the columns of the PSSM matrix are merged to obtain an L*10 matrix. Then,
#'with a relationship similar to the auto covariance transformation feature, this feature with a length of 110 is
#'obtained from this matrix.
#' @param pssm_name name of PSSM Matrix file
#' @import utils
#' @return feature vector of length 110
#' @references
#' Ding, S., et al. (2014) A protein structural classes prediction method based on predicted secondary structure and
#' PSI-BLAST profile, Biochimie, 97, 60-65.
#' @export
#' @examples
#' X<-rpssm(system.file("extdata", "C7GQS7.txt.pssm", package="PSSMCOOL"))
rpssm<-function(pssm_name){
x<-read.delim(pssm_name,skip = 2,sep = "",header = FALSE)
x<-x[-1,-c(1,23:44)]
d<-which(x=="Lambda")
if(length(d)!=0){
x<-x[-c(d:dim(x)[1]),]
}
x<-x[,-1]
colnames(x)<-NULL
rownames(x)<-NULL
x<-as.matrix(x)
mode(x)<-"integer"
m2<-x
L<-dim(m2)[1]
#m2<-1/(1+exp(-m2))
p<-matrix(0,L,10)
p[,1]<-(m2[,14]+m2[,19]+m2[,18])/3
p[,2]<-(m2[,13]+m2[,11])/2
p[,3]<-(m2[,10]+m2[,20])/2
p[,4]<-(m2[,1]+m2[,17]+m2[,16])/3
p[,5]<-(m2[,3]+m2[,9])/2
p[,6]<-(m2[,6]+m2[,7]+m2[,4])/3
p[,7]<-(m2[,2]+m2[,12])/2
p[,8]<-m2[,5]
p[,9]<-m2[,8]
p[,10]<-m2[,15]
x<-apply(p,2,mean)
names(x)<-NULL
D<-rep(0,10)
for(j in 1:10){
for(i in 1:L){
D[j]<-D[j]+(p[i,j]-x[j])^2
}
}
D<-(1/L)*D
DD<-matrix(0,10,10)
for(s in 1:10){
for(t in 1:10){
for(i in 1:(L-1)){
DD[s,t]<-DD[s,t]+((p[i,s]-p[i+1,t])^2)/2
}
}
}
DD<-DD/(L-1)
v<-c()
for(i in 1:10){
v<-c(v,DD[i,])
}
v<-c(v,D)
v<-round(v,digits = 4)
return(v)
}
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