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To understand the function of the encoded proteins, we need to be able to know the subcellular location of a protein. The most common method used for determining subcellular location is fluorescence microscopy which allows subcellular localizations to be imaged in high throughput. Image feature calculation has proven invaluable in the automated analysis of cellular images. This article proposes a novel method named LDPs for feature extraction based on invariant of translation and rotation from given images, the nature which is to count the local difference features of images, and the difference features are given by calculating the D-value between the gray value of the central pixel c and the gray values of eight pixels in the neighborhood. The novel method is tested on two image sets, the first set is which fluorescently tagged protein was endogenously expressed in 10 sebcellular locations, and the second set is which protein was transfected in 11 locations. A SVM was trained and tested for each image set and classification accuracies of 96.7 and 92.3 % were obtained on the endogenous and transfected sets respectively. [ABSTRACT FROM AUTHOR]

A novel approach CE-Ploc is proposed for predicting protein subcellular locations by exploiting diversity both in feature and decision spaces. The diversity in a sequence of feature spaces is exploited using hydrophobicity and hydrophilicity of amphiphilic pseudo amino acid composition and a specific learning mechanism. Diversity in learning mechanisms is exploited by fusion of classifiers that are based on different learning mechanisms. Significant improvement in prediction performance is observed using jackknife and independent dataset tests. [ABSTRACT FROM AUTHOR]