CS_PSe_PSSM {PSSMCOOL}

CSP-SegPseP-SegACP feature vector

Description

This feature vector is constructed by fusing consensus sequence (CS), segmented PsePSSM, and segmented auto-covariance transformation (ACT) based on PSSM. by consensus sequence a 40-dimensional feature vector is obtained, in segmented PsePSSM group, by dividing PSSM Matrix to 2 and 3 segments a 380-dimensional feature vector is obtained and in ACT group, similar to the previous group at first PSSM Matrix is divided to 2 and 3 segments then a feature vector of length 280 is obtained.eventually by fusing these features a 700-dimensional feature vector is obtained.

Usage

CS_PSe_PSSM(pssm_name, vec_name)

Arguments

pssm_name

name of PSSM Matrix file

vec_name

a character that user imports to specify kind of feature vector which it can be varied between four values

Details

If vec_name equals to "segmented_psepssm" then a feature vector of length 380 is obtained. if vec_name equals to "segmented_acpssm" then a feature vector of length 280 is obtained, and if vec_name equals to "cspssm" the obtained feature vector would be of length 40 eventually if vec_name equals to "total" then feature vector would be of length 700.

Value

feature vector that its length depends on the vec_name which user imports. vec_name can be one of "cspssm", "segmented_psepssm", "segmented_acpssm", "total".

References

Y. Liang, S. Liu, S. J. C. Zhang, and m. m. i. medicine, "Prediction of protein structural classes for low-similarity sequences based on consensus sequence and segmented PSSM," vol. 2015, 2015.

Examples

X<-CS_PSe_PSSM(system.file("extdata", "C7GSI6.txt.pssm", package="PSSMCOOL"),"total")