TY  - JOUR
N1  - © 2008 Verma et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ID  - open604
UR  - http://www.biomedcentral.com/1471-2105/9/201/
A1  - Verma, Ruchi
A1  - Tiwari, Ajit
A1  - Kaur, Sukhwinder
A1  - Varshney, Grish C
A1  - Raghava, G.P.S.
Y1  - 2008///
N2  - This study demonstrates that secretory proteins have different residue composition than non-secretory proteins. Thus, it is possible to predict secretory proteins from its residue composition-using machine learning technique. The multiple sequence alignment provides more information than sequence itself. Thus performance of method based on PSSM profile is more accurate than method based on sequence composition. A web server PSEApred has been developed for predicting secretory proteins of malaria parasites,the URL can be found in the Availability and requirements section.
PB  - BIomedcentral
JF  - BMC bioinformatics
VL  - 9
SN  - 1471-2105
TI  - Identification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles.
SP  - 1
AV  - public
EP  - 11
ER  -