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Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we design seven different training functions based on Elman networks, and use a genetic algorithm to select the proper networks for an ensemble. Experimental results show that the neural networks ensemble has a dominant advantage in performance.