TY  - JOUR
N1  - Copyright of this article belongs to OUP.
Supplementary information
http://www.imtech.res.in/raghava/pslpred/supl.html
ID  - open190
UR  - http://bioinformatics.oxfordjournals.org/content/21/10/2522.full.pdf+html
IS  - 10
A1  - Bhasin, Manoj
A1  - Garg, Aarti
A1  - Raghava, G.P.S.
Y1  - 2005/05/15/
N2  - SUMMARY: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5. AVAILABILITY: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/
PB  - Oxford University press
JF  - Bioinformatics (Oxford, England)
VL  - 21
SN  - 1367-4803
TI  - PSLpred: prediction of subcellular localization of bacterial proteins.
SP  - 2522
AV  - restricted
EP  - 4
ER  -