UC Riverside

Essential genes are genes that critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. Here we propose a deep neural network (DNN) for predicting essential genes in microbes. Our DNN-based architecture called D eeply E ssential makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction, thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. Our extensive experimental results show that D eeply E ssential outperforms existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. We also expose and study a hidden performance bias that affected previous classifiers. The code of D eeply E ssential is freely available at https://github.com/ucrbioinfo/DeeplyEssential