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#' auto covariance transformation feature vector
#' @description The AC variable measures the correlation of the same property between two residues
#'separated by a distance of lg along the sequence
#' @param pssm_name name of the PSSM Matrix file
#' @param lg a parameter which indicates distance between two residues
#' @note in use of this function The lg parameter must be less than the length of the smallest sequence
#' in the database.
#' @import utils
#' @return feature vector which its length depends on parameter lg. by default lg is 10 hence feature vector
#' would be of length 200.
#' @references
#' Dong, Q., Zhou, S. and Guan, J. (2009) A new taxonomy-based protein fold recognition approach
#' based on autocross-covariance transformation, Bioinformatics, 25, 2655-2662.
#' @export
#' @examples
#' X<-pssm_ac(system.file("extdata", "C7GQS7.txt.pssm", package="PSSMCOOL"))
pssm_ac <- function(pssm_name,lg=10){ #lg smaler than shortest protein length in database
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"
s<-x
#s<-1/(1+exp(-s))
L<-dim(s)[1]
sbar<-apply(s,2,mean)
names(sbar)<-NULL
sbar<-round(sbar,digits = 4)
AC<-matrix(0,nrow = lg,ncol = 20)
g<-0
for(t in 1:lg){
for(j in 1:20){
for (i in 1:(L-t)) {
g<-g+(s[i,j]-sbar[j])*(s[i+t,j]-sbar[j])
}
AC[t,j]<-g/(L-t)
g<-0
}
}
vec<-c()
for(i in 1:lg){
vec<-c(vec,AC[i,])
}
vec<-round(vec,digits = 4)
return(vec)
}
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