title: 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 across organisms. An independent dataset fromSynechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Hong-Li
authors.initials: HL
authors.lastname: Hua
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Fa-Zhan
authors.initials: FZ
authors.lastname: Zhang
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Abraham Alemayehu
authors.initials: AA
authors.lastname: Labena
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Chuan
authors.initials: C
authors.lastname: Dong
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Yan-Ting
authors.initials: YT
authors.lastname: Jin
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Feng-Biao
authors.identifier: 0000-0002-1129-5233
authors.initials: FB
authors.lastname: Guo