Performance analysis of classifying localization sites of protein using data mining techniques and artificial neural networks

Protein localization prediction is computation approach to predict where a protein resides in a cell. Accurate localization of proteins is needed to provide physiological substance for their function and aberrant localization of protein causes pathogenesis of various human diseases. E.Cott and Yeast are unicellular organism and different proteins allocate in their cell. If those protein are dislocated, then these causes various infections that affected human body adversely. So, the objective of this work is to classify proteins into different cellular localization sites based on amino acid sequences of E.Coli bacterium and Yeast In this experiment, we collect dataset of E.Coli and Yeast from data repository and preprocessed it for further processing. Then we train our dataset with several data mining classification algorithms and artificial neural networks. After classifying both dataset, we compare accuracies among different classifiers and try to find best classifiers for Protein localization sites prediction of E.Coli and Yeast dataset.