A computational framework based on ensemble deep neural networks for essential genes identification

Nguyen Quoc Khanh Le, Duyen Thi Do, Truong Nguyen Khanh Hung, Luu Ho Thanh Lam, Tuan Tu Huynh, Ngan Thi Kim Nguyen

研究成果: 雜誌貢獻文章同行評審

44 引文 斯高帕斯(Scopus)


Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state‐-of‐-the‐-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.
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期刊International journal of molecular sciences
出版狀態已發佈 - 12月 1 2020

ASJC Scopus subject areas

  • 催化
  • 分子生物學
  • 光譜
  • 電腦科學應用
  • 物理與理論化學
  • 有機化學
  • 無機化學


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