Performance evaluation of features for gene essentiality prediction

Abstract

Essential genes are subset of genes required by an organism for growth and sustenance of life and as well responsible for phenotypic changes when their activities are altered. They have been utilized as drug targets, disease control agent, etc. Essential genes have been widely identified especially in microorganisms, due to the extensive experimental studies on some of them such as Escherichia coli and Saccharomyces cerevisiae. Experimental approach has been a reliable method to identify essential genes. However, it is complex, costly, labour and time intensive. Therefore, computational approach has been developed to complement the experimental approach in order to minimize resources required for essentiality identification experiments. Machine learning approaches have been widely used to predict essential genes in model organisms using different categories of features with varying degrees of accuracy and performance. However, previous studies have not established the most important categories of features that provide the distinguishing power in machine learning essentiality predictions. Therefore, this study evaluates the discriminating strength of major categories of features used in essential gene prediction task as well as the factors responsible for effective computational prediction. Four categories of features were considered and k- fold cross-validation machine learning technique was used to build the classification model. Our results show that ontology features with an AUROC score of 0.936 has the most discriminating power to classify essential and non-essential genes. This studyconcludes that more ontology related features will further improve the performance of machine learning approach and also sensitivity, precision and AUPRC are realistic measures of performance in essentiality prediction.