Escherichia coli: Analysis of Features for Protein Localization Classification Employing Fusion Data: Analysis of Features for Protein Localization Classification Employing Fusion Data

Machine learning models can be used for relevance of features in classification systems. The interest in protein analysis based on biomolecular information has rapidly grown. In this case a comparison of two sources of this information was employed to determine protein localization in Escherichia coli cells. Models as support vector machines, artificial neural networks and random forest were compared for the prediction of protein localization. The sources of data used to train the models were the information from targeting signal and protein sequences, for determining the localization sites of the protein. A third scenario with a fusion of both sources of data was employed. Four classes were established according to the subcellular localization of the protein: cytoplasm, periplasmatic space, outer and inner membranes. Results reached values between 77% and 92% in terms of balanced accuracy. The models with better performance were based on random forest and support vector machines. In terms of features, the first source, where targeting signal was employed, was the one with best performance associated to relevance for the classification.