Prediction of essential genes by mining gene ontology semantics

Essential genes are indispensable for an organism's living. These genes are widely discussed, and many researchers proposed prediction methods that not only find essential genes but also assist pathogens discovery and drug development. However, few studies utilized the relationship between gene functions and essential genes for essential gene prediction. In this paper, we explore the topic of essential gene prediction by adopting the association rule mining technique with Gene Ontology semantic analysis. First, we proposed two features named GOARC (Gene Ontology Association Rule Confidence) and GOCBA (Gene Ontology Classification Based on Association), which are used to enhance the classifier constructed with the features commonly used in previous studies. Secondly, we use an association-based classification algorithm without rule pruning for predicting essential genes. Through experimental evaluations and semantic analysis, our methods can not only enhance the accuracy of essential gene prediction but also facilitate the understanding of the essential genes' semantics in gene functions.