DeeplyEssential: a deep neural network for predicting essential genes in microbes
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RESEARCH
Open Access
DeeplyEssential: a deep neural network for predicting essential genes in microbes Md Abid Hasan* and Stefano Lonardi From The Sixth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2019) Niagara Falls, NY, USA. 07 September 2019 *Correspondence: [email protected] Department of Computer Science and Engineering, University of California Riverside, 900 University Ave, 92507 Riverside, CA, USA
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. Keywords: Essential genes, Deep neural network, Microbes, Data leak
Background Essential genes are those genes that are critical for the survival and reproduction of an organism [1]. Since the disruption of essential genes induces the death of an organism, the identification of essential genes can provide targets for new antimicrobial/antibiotic drugs [2, 3]. Essential genes are also critical for the creation of artificial self-sustainable living cells with a minimal genome [4]. Finally, essential genes have been a cornerstone in the study of the origin and evolution of organisms [5]. The identification of essential genes via wet-lab experiments is labor intensive, expensive and time-consuming. Such experimental procedures include single gene knock-out
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