pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC-Reference-Cited by-同舟云学术

pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC

Published:2018-07-13 Issue:3 Volume:35 Page:398-406
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Cheng Xiang¹²,Lin Wei-Zhong¹,Xiao Xuan¹²,Chou Kuo-Chen²³

Affiliation:

1. Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China

2. Computational Biology, Gordon Life Science Institute, Boston, MA, USA

3. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China

Province National Natural Science Foundation of JiangXi

Department of Education of JiangXi Province

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/35/3/398/27700248/bty628.pdf

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3. iMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition;Arif;J. Theor. Biol.,2018

4. Using LogitBoost classifier to predict protein structural classes;Cai;J. Theor. Biol.,2006

5. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect;Cai;J. Cell. Biochem.,2002

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