Comput Biol Chem - CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition.

Tópicos

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Resumo

Precise information about protein locations in a cell facilitates in the understanding of the function of a protein and its interaction in the cellular environment. This information further helps in the study of the specific metabolic pathways and other biological processes. We propose an ensemble approach called "CE-PLoc" for predicting subcellular locations based on fusion of individual classifiers. The proposed approach utilizes features obtained from both dipeptide composition (DC) and amphiphilic pseudo amino acid composition (PseAAC) based feature extraction strategies. Different feature spaces are obtained by varying the dimensionality using PseAAC for a selected base learner. The performance of the individual learning mechanisms such as support vector machine, nearest neighbor, probabilistic neural network, covariant discriminant, which are trained using PseAAC based features is first analyzed. Classifiers are developed using same learning mechanism but trained on PseAAC based feature spaces of varying dimensions. These classifiers are combined through voting strategy and an improvement in prediction performance is achieved. Prediction performance is further enhanced by developing CE-PLoc through the combination of different learning mechanisms trained on both DC based feature space and PseAAC based feature spaces of varying dimensions. The predictive performance of proposed CE-PLoc is evaluated for two benchmark datasets of protein subcellular locations using accuracy, MCC, and Q-statistics. Using the jackknife test, prediction accuracies of 81.47 and 83.99% are obtained for 12 and 14 subcellular locations datasets, respectively. In case of independent dataset test, prediction accuracies are 87.04 and 87.33% for 12 and 14 class datasets, respectively.

Resumo Limpo

precis inform protein locat cell facilit understand function protein interact cellular environ inform help studi specif metabol pathway biolog process propos ensembl approach call ceploc predict subcellular locat base fusion individu classifi propos approach util featur obtain dipeptid composit dc amphiphil pseudo amino acid composit pseaac base featur extract strategi differ featur space obtain vari dimension use pseaac select base learner perform individu learn mechan support vector machin nearest neighbor probabilist neural network covari discrimin train use pseaac base featur first analyz classifi develop use learn mechan train pseaac base featur space vari dimens classifi combin vote strategi improv predict perform achiev predict perform enhanc develop ceploc combin differ learn mechan train dc base featur space pseaac base featur space vari dimens predict perform propos ceploc evalu two benchmark dataset protein subcellular locat use accuraci mcc qstatist use jackknif test predict accuraci obtain subcellular locat dataset respect case independ dataset test predict accuraci class dataset respect

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