Abstract
Essential genes are indispensable for biological survival. Thus it is of great significance to identify and study essential genes. A machine learning method, K-Nearest Neighbor, is used for development of predicting essential bacterial genes. The homologous features, including sequence homology and functional homology, of the bacterial genomes are extracted for determining essential genes. Based on the features, we use K-Nearest Neighbor algorithm for determining of gene function. And we tune the minimum matching parameter (K) in the essential gene predicted model for building an optimal model of the Escherichia coli specificity model. The corresponding optimal parameter (K) is then extended to other bacterial essential genes predicting models. After cross validation, the highest accuracy is 0.89 while K between 5 and 7. Therefore, the features we extracted can increase the accuracy of the bacterial essential gene prediction. In the premise, we found that the prediction accuracy of the prediction model based on K-Nearest Neighbor was not significantly different in different evolutionary distances between organisms in the database and the investigated species. That means the machine learning model can be extended to more distant species. It wills have a better predictive performance for predicting essential genes of distant species than the usual sequence-based methods.
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Acknowledgement
This study was jointly funded by the National Natural Science Foundation of China (61803112), the Science and Technology Foundation of Guizhou Province (2018–1133, 2019–2811), the Science and Technology Foundation of Guiyang (2017–30-15), the Science and Technology Fund project of Guizhou Health Commission (gzwjkj2019–1-40), and the Cell and Gene Engineering Innovative Research Groups of Guizhou Province (KY-2016–031).
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Ye, Y., Liang, D., Zeng, Z. (2022). The Algorithms of Predicting Bacterial Essential Genes and NcRNAs by Machine Learning. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_54
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DOI: https://doi.org/10.1007/978-981-16-6554-7_54
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