2nd International Conference On Systems Engineering and Modeling (ICSEM-13)

Predicting Protein Subcellular Localization Using the Algorithm of Increment Of Diversity Combined with Weighted K-Nearest Neighbor

Authors
Zeyue Wu, Yuehui Chen
Corresponding Author
Zeyue Wu
Available Online April 2013.
DOI
https://doi.org/10.2991/icsem.2013.117How to use a DOI?
Keywords
subcellular localization, feature extraction, increment of diversity,Weighted K-Nearest Neighbor
Abstract
Protein subcellular localization is an important research field of bioinformatics. In this paper, we use the algorithm of the increment of diversity combined with weighted K nearest neighbor to predict protein in SNL6 which has six subcelluar localizations and SNL9 which has nine subcelluar localizations. We use the increment of diversity to extract diversity finite coefficient as new features of proteins. And the basic classifier is weighted K-nearest neighbor. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 83.3% for SNL6 and 87.6 % for SNL9. By comparing its results with other methods, it indicates the new approach is feasible and effective.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference On Systems Engineering and Modeling (ICSEM-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2013
ISBN
978-94-91216-42-8
DOI
https://doi.org/10.2991/icsem.2013.117How 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  - 2013/04
DA  - 2013/04
TI  - Predicting Protein Subcellular Localization Using the Algorithm of Increment Of Diversity Combined with Weighted K-Nearest Neighbor
BT  - 2nd International Conference On Systems Engineering and Modeling (ICSEM-13)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/icsem.2013.117
DO  - https://doi.org/10.2991/icsem.2013.117
ID  - Wu2013/04
ER  -