An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

ABSTRACT: Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions acro

SUBMITTER: Hua HL 

PROVIDER: S-EPMC5021884 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature