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

DOI

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 590
EP  - 594
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.117
DO  - 10.2991/icsem.2013.117
ID  - Wu2013/04
ER  -