期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2019
卷号：17
页码：785-796
DOI：10.1016/j.csbj.2019.05.008
出版社：Computational and Structural Biotechnology Journal
摘要：The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes, and then employed a systematic machine-learning approach, using protein sequence-derived features and selection procedures, to investigate essential gene predictions within and among species. We showed that the numbers of essential gene orthologs comprise small fractions when compared with the total number of orthologs among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trained with subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species. The present work provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes using machine learning approaches.
关键词：Machine-learning ; Essential genes ; Essentiality prediction ; Eukaryotes ; ML Machine-learning ; RNAi RNA interference ; CRISPR Clustered regularly interspaced short palindromic repeats ; PPI Protein-protein interaction ; GI Genetic interaction ; SPLS Sparse partial least squares ; OGEE Online GEne essentiality database ; GO Gene ontology ; GLM Generalised linear model ; NN Artificial neural network ; GBM Gradient boosting method ; SVM Support-Vector machine ; RF Random Forest ; ROC-AUC Area under the receiver operating characteristic curve ; PR-AUC Area under the precision-recall curve
