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Title: DeeplyEssential: a deep neural network for predicting essential genes in microbes
Abstract Background Essential 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. Results We 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. Conclusion Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.  more » « less
Award ID(s):
1814359
NSF-PAR ID:
10249205
Author(s) / Creator(s):
;
Date Published:
Journal Name:
BMC Bioinformatics
Volume:
21
Issue:
S14
ISSN:
1471-2105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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