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DeeplyEssential: a deep neural network for predicting essential genes in microbes
BMC Bioinformatics ( IF 3 ) Pub Date : 2020-09-30 , DOI: 10.1186/s12859-020-03688-y
Md Abid Hasan , Stefano Lonardi

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. 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. Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.

中文翻译:

DeeplyEssential:深层神经网络,用于预测微生物中的必需基因

必需基因是对生物体生存至关重要的那些基因。细菌中必需基因的预测可以为设计新型抗生素化合物或抗菌策略提供目标。我们提出了一个深层神经网络,用于预测微生物中的必需基因。我们的称为DeeplyEssential的体系结构对输入数据进行了最小假设(即,它仅使用基因主序列和相应的蛋白质序列)来进行预测,因此与需要结构或拓扑特征的现有预测变量相比,它可以最大限度地提高其实际应用价值。一应俱全。我们还将揭露并研究影响先前分类器的隐藏性能偏差。大量结果表明,DeeplyEssential优于使用降采样来平衡训练集或使用聚类来排除直系同源基因多个副本的现有分类器。深度神经网络体系结构可以仅使用序列信息来有效预测微生物基因是否必不可少。
更新日期:2020-09-30
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