ArticleGenes & genomics2020
Prediction of essential genes in prokaryote based on artificial neural network.
Article in Genes & genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- CNN4Essential: a convolutional neural network model for predicting bacterial gene essentiality based on multi-feature fusion.BMC genomics · 2026Article
- A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.BMC biology · 2025Article
- Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification.Frontiers in genetics · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid identification of new essential genes is necessary to understand biological mechanisms and identify potential targets for antimicrobial drugs. Many computational methods have been proposed.
objectivesTo construct an essential genes classifier which satisfies more different organisms, and to study the redundancy of features used in the prediction of essential genes.
methodsWe designed a 57-12-1 artificial neural network model to predict the essential genes of 31 prokaryotic genomes. Four methods including self-predictions of each organism, the leave-one-genome-out method, predicting all by one organism, and self-predictions of all organisms were applied to assess the predictive performance. Additionally, the 57 features used in the artificial neural network model were analyzed by weighted principal component analysis to screen the key features strongly related to the essentiality of genes.
resultsOur results compared with previous researches indicate that our models had better generalizability. Furthermore, this method reduced the features to 29 while maintaining stable prediction performance overall, suggesting that some features are redundant for gene essentiality, and the screened features contained more important biological information for gene essentiality.
conclusionThis study showed the effectiveness and generalizability of our artificial neural network model. In addition, the screened features could be used as key features in computational analysis and biological experiments.
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