Predicting Protein Subcellular Localization Using the Algorithm of Increment Of Diversity Combined with Weighted K-Nearest Neighbor
Zeyue Wu, Yuehui Chen
Available Online April 2013.
- https://doi.org/10.2991/icsem.2013.117How to use a DOI?
- subcellular localization, feature extraction, increment of diversity,Weighted K-Nearest Neighbor
- 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.
- 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 SP - 590 EP - 594 SN - 1951-6851 UR - https://doi.org/10.2991/icsem.2013.117 DO - https://doi.org/10.2991/icsem.2013.117 ID - Wu2013/04 ER -