DocumentCode :
553202
Title :
A comparative study on sequence feature extraction for type III secreted effector prediction
Author :
Yang Yang
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Maritime Univ., Shanghai, China
Volume :
3
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
1560
Lastpage :
1564
Abstract :
Protein secretion is an essential mechanism for bacterial survival in their surrounding environment. The type III secretion system (T3SS) is a specialized protein delivery system that plays a key role in pathogens. Since the secretion mechanism has not been fully understood yet, T3SS has attracted a great deal of research interests. Especially, the identification of novel effectors (secreted proteins) is an important and challenging task for the T3SS study. This paper adopts machine learning methods to predict type III secreted effectors (T3SE). We conduct a comparative study on the feature extraction methods for protein sequence of T3SEs, and propose new methods involving sequence features, secondary structure and solvent accessibility information. The experimental results on Pseudomonas syringae data set demonstrate the effectiveness of our methods.
Keywords :
biology computing; feature extraction; learning (artificial intelligence); molecular biophysics; proteins; T3SS study; accessibility information; bacterial survival; machine learning method; pathogens; protein delivery system; protein secretion; protein sequence feature; pseudomonas syringae data set; secondary structure; secreted effector identification; secretion mechanism; sequence feature extraction method; type III secreted effector prediction; Accuracy; Amino acids; Bioinformatics; Feature extraction; Microorganisms; Proteins; Solvents;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
Type :
conf
DOI :
10.1109/FSKD.2011.6019870
Filename :
6019870
Link To Document :
بازگشت