Abstract
Subcellular localization is a well-designed representation of proteins. We need a fully automatic and reliable prediction system for protein subcellular localization, especially for the analysis of large-scale of yeast microarray data. In this paper we consider the dataset with multi classes and propose the classification for each location of protein subcellular in a separate layer. In this work, a multi-classification approach for subcellular localization is designed and developed to achieve high efficiency and improve the prediction and classification accuracy. The rule based Ripper method has been found to predict the subcellular localization of proteins from their protein microarray data, compared to other classifiers.Keywords: Data Mining, Microarray, Classification, Layered Approach, Protein Subcellular Localization.
Highlights
Genome function annotation including the assignment of a function for a potential gene in the raw sequence is the hot topic in bioinformatics
Numerous stabs have been made to predict protein subcellular localization. Maximum of these prediction method scan be classified into two categories: one is based on the recognition of protein N-terminal sorting signals and the other is based on amino acid composition [1]
This paper introduces a new prediction method for protein subcellular localization based on yeast dataset
Summary
Genome function annotation including the assignment of a function for a potential gene in the raw sequence is the hot topic in bioinformatics. Numerous stabs have been made to predict protein subcellular localization Maximum of these prediction method scan be classified into two categories: one is based on the recognition of protein N-terminal sorting signals and the other is based on amino acid composition [1]. They proposed an integrated prediction system for subcellular localization using neural networks based on individual sorting signal predictions or Support Vector Mechanism for general purpose supervised pattern recognition. We construct a prediction system for subcellular localization called Ripper based on the Data mining classification method. The results show that the prediction accuracy is significantly improved with this novel method and the method is very robust to errors in the yeast microarray data [2]
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