A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.
Int J Mol Sci
; 21(23)2020 Nov 28.
Article
em En
| MEDLINE
| ID: mdl-33260643
ABSTRACT
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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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Redes Neurais de Computação
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Genes Essenciais
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Aprendizado Profundo
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article