Predicting Protein Subcellular Localization Using the Algorithm of Increment of Diversity Combined with 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.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 3099-3103
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.3099
• Online since: September 2013
• Authors: Ze Yue Wu, Yue Hui Chen
• Keywords: Feature Extraction, Increment of Diversity, Subcellular Localization, Weighted K-Nearest Neighbor

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