A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

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 biologi

SUBMITTER: Le NQK 

PROVIDER: S-EPMC7730808 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature