| swMATH ID: |
25079
|
| Software Authors: |
Khan, M.; Hayat, M.; Khan, S. A.; Iqbal, N.
|
| Description: |
Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou’s general PseAAC. This study investigates an efficient and accurate computational method for predicating mycobacterial membrane protein. Mycobacterium is a pathogenic bacterium which is the causative agent of tuberculosis and leprosy. The existing feature encoding algorithms for protein sequence representation such as composition and translation, and split amino acid composition cannot suitably express the mycobacterium membrane protein and their types due to biasness among different types. Therefore, in this study a novel un-biased dipeptide composition (Unb-DPC) method is proposed. The proposed encoding scheme has two advantages, first it avoid the biasness among the different mycobacterium membrane protein and their types. Secondly, the method is fast and preserves protein sequence structure information. The experimental results yield SVM based classification accurately of 97.1 |
| Homepage: |
https://www.sciencedirect.com/science/article/pii/S0022519316304143
|
| Related Software: |
2L-piRNA;
iRSpot-EL;
iRNA-PseColl;
Pse-in-One;
pLoc-mAnimal;
iATC-mHyb;
PseKNC;
iRNA-AI;
pLoc-mEuk;
pLoc-mVirus;
pLoc-mPlant;
iSNO-PseAAC;
iPPI-Esml;
iPTM-mLys;
iCar-PseCp;
Gneg-mPLoc;
OOgenesis_Pred;
POSSUM;
iRNAm5C-PseDNC;
iPPBS-Opt
|
| Cited in: |
7 Documents
|
