Training set selection for the prediction of essential genes.
Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.
College of Life Sciences and State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, Shaanxi, China ; Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi, China.
Jian
J
Cheng
College of Science, Northwest A&F University, Yangling Shaanxi, China.
Zhao
Z
Xu
Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi, China ; Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
Wenwu
W
Wu
College of Life Sciences and State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, Shaanxi, China ; Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi, China.
Li
L
Zhao
College of Life Sciences and State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, Shaanxi, China ; Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi, China.
Xiangchen
X
Li
College of Wine, Northwest A&F University, Yangling Shaanxi, China.
Yanlin
Y
Liu
College of Life Sciences and State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, Shaanxi, China ; Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi, China.
Shiheng
S
Tao
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D020043:Genes, Essential / Q000235:genetics*; D008957:Models, Genetic; D058977:Molecular Sequence Annotation / Q000379:methods*; D010802:Phylogeny; D013045:Species Specificity