Predicting essential genes of 37 prokaryotes by combining information-theoretic features.

Essential genes are required for the reproduction and survival of an organism. Rapid identification of essential genes has practical application value in biomedicine. Information theory is a discipline that studies information transmission. Based on the similarity between heredity and information transmission, measures derived from information theory can be applied to genetic sequence analysis on different scales. In this study, we employed 114 features extracted by information theory methods to construct an essential gene prediction model. We applied a backpropagation neural network to construct a classifier and employed it to predict essential genes of 37 prokaryotes. The performance of the classifier was evaluated by applying intra-organism prediction and leave-one-species-out prediction. Among 37 prokaryotes, intra-organism prediction and leave-one-species-out prediction yielded average AUC scores of 0.791 and 0.717, respectively. Considering the potential redundancy in the feature set, we performed feature selection and constructed a key feature subset. In the above two prediction methods, the average AUC scores of 37 organisms obtained by using key features were 0.786 and 0.714, respectively. The results show the potential and universality of information-theoretic features in the study of prokaryotic essential gene prediction.