Efficient Prediction System Research Articles

Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images

Discriminative feature extraction technique is always required for the development of accurate and efficient prediction systems for protein subcellular localization so that effective drugs can be developed. In this work, we showed that Local Ternary Patterns (LTPs) effectively exploit small variations in pixel intensities; present in fluorescence microscopy based protein images of human and hamster cell lines. Further, Synthetic Minority Oversampling Technique is applied to balance the feature space for the classification stage. We observed that LTPs coupled with data balancing technique could enable a classifier, in this case support vector machine, to yield good performance. The proposed ensemble based prediction system, using 10-fold cross-validation, has yielded better performance compared to existing techniques in predicting various subcellular compartments for both 2D HeLa and CHO datasets. The proposed predictor is available online at: http://111.68.99.218/Protein_SubLoc/, which is freely accessible to the public.

Two-stage Hybrid Classifier Ensembles for Subcellular Phenotype Images Classification

An automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we propose a two-stage multiple classifier system to improve classification reliability by introducing rejection option. The system is built as a cascade of two classifier ensembles. The first ensemble consists of set of binary SVMs which generalizes to learn a general classification rule and the second ensemble focus on the exceptions rejected by the rule. To enhance diversity for the classifier ensembles, multiple features are introduced, including the local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM). Using the public benchmark 2D HeLa cell images, a high classification accuracy 96% is obtained with rejection rate 21%.

PROTEIN SUBCELLULAR LOCALIZATION BASED ON PSI-BLAST AND MACHINE LEARNING

Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.