DeeplyEssential: a deep neural network for predicting essential genes in microbes

BMC Bioinformatics

Md Abid Hasan¹  Stefano Lonardi¹ 

[1] Department of Computer Science and Engineering, University of California Riverside, 900 University Ave, 92507, Riverside, CA, USA;

关键词: Essential genes;    Deep neural network;    Microbes;    Data leak;   

DOI  :  10.1186/s12859-020-03688-y

来源: Springer

【 摘 要 】

BackgroundEssential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies.ResultsWe propose a deep neural network for predicting essential genes in microbes. Our architecture called DeeplyEssential makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DeeplyEssential outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes.ConclusionDeep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.

【 授权许可】

CC BY