Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)

Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network

Authors
Zeyue Wu, Yuehui Chen
Corresponding Author
Zeyue Wu
Available Online May 2014.
DOI
https://doi.org/10.2991/iccia.2012.70How to use a DOI?
Keywords
subcellular localization, feature extraction, artificial neural network, ECOC
Abstract
Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible and effective.
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Proceedings
Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
Part of series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-91216-41-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccia.2012.70How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zeyue Wu
AU  - Yuehui Chen
PY  - 2014/05
DA  - 2014/05
TI  - Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 287
EP  - 290
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.70
DO  - https://doi.org/10.2991/iccia.2012.70
ID  - Wu2014/05
ER  -