MultiNNProm: A Multi-Classifier System for Finding Genes

The computational identification of genes in DNA sequences has become an issue of crucial importance due to the large number of DNA molecules being currently sequenced. We present a novel neural network based multi-classifier system, MultiNNProm, for the identification of promoter regions in E.Coli DNA sequences. The DNA sequences were encoded using four different encoding methods and were used to train four different neural networks. The classification results of these neural networks were then aggregated using a variation of the LOP method. The aggregating weights used within the modified LOP aggregating algorithm were obtained through a genetic algorithm. We show that the use of different neural networks, trained on the same set of data, could provide slightly varying results if the data were differently encoded. We also show that the combination of more neural classifiers provides us with better accuracy than the individual networks.