DocumentCode :
2831508
Title :
Methods for Protein Subcellular Localization Prediction
Author :
Juan, Eric Y T ; Chang, J.H. ; Li, C.H. ; Chen, B.Y.
Author_Institution :
Dept. of Comput. Sci. & Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan
fYear :
2011
fDate :
June 30 2011-July 2 2011
Firstpage :
553
Lastpage :
558
Abstract :
Large-scale protein analysis and reliable annotations are particularly helpful for scholars in biology and medicine community. Understanding the functional characterizations of protein sequences has been a major challenge in recent years. Extensive computer based prediction systems have been developed to support the need since many proteins´ sub cellular localizations are still unknown. In this work, numerous protein description methods and three common classifiers are used in our experiments. These protein description methods are classified into two categories: protein composition based and position-specific scoring matrix based. A better prediction is achieved upon widely used data sets. Through these experiments, it is expected to give a comparison between protein description methods for protein sub cellular localization and their classification characteristics.
Keywords :
biology computing; pattern classification; proteins; biology community; classification characteristics; computer based prediction systems; medicine community; position specific scoring matrix; protein composition; protein sequences; protein subcellular localization prediction; Accuracy; Amino acids; Data models; Erbium; Predictive models; Proteins; Support vector machines; classifier; positionspecific scoring matrix; protein subcellular localizati;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-61284-709-2
Electronic_ISBN :
978-0-7695-4373-4
Type :
conf
DOI :
10.1109/CISIS.2011.91
Filename :
5989069
Link To Document :
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