Prediction of Protein Subcellular Locations by Combining K Local Hyperplane Distance Nearest Neighbor 1st Edition by Hong Liu, Haodi Feng, Daming Zhu 9783540738701

Prediction of Protein Subcellular Locations by Combining K Local Hyperplane Distance Nearest Neighbor 1st Edition by Hong Liu, Haodi Feng, Daming Zhu – Ebook PDF Instant Download/Delivery. 9783540738701

Full download Prediction of Protein Subcellular Locations by Combining K Local Hyperplane Distance Nearest Neighbor 1st Edition after payment

Product details:

ISBN 13: 9783540738701

Author: Hong Liu, Haodi Feng; Daming Zhu

A huge number of protein sequences have been generated and collected. However, the functions of most of them are still unknown. Protein subcellular localization is important to elucidate protein function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been done to accomplish such a task, there is the need for further research to improve the accuracy of prediction. In this paper, with K-local Hyperplane Distance Nearest Neighbor algorithm (HKNN) as base classifier, an ensemble classifier is proposed to predict the subcellular locations of proteins in eukaryotic cells. Each basic HKNN classifiers are constructed from a separated feature set, and finally combined with majority voting scheme. Results obtained through 5-fold cross-validation test on the same protein dataset showed an improvement in pre-diction accuracy over existing algorithms.

People also search for Prediction of Protein Subcellular Locations by Combining K Local Hyperplane Distance Nearest Neighbor 1st: 

protein subcellular localization prediction

subcellular localisation of proteins

prediction of subcellular localization

 subcellular localization