ArticlePloS one2017
Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- CNN4Essential: a convolutional neural network model for predicting bacterial gene essentiality based on multi-feature fusion.BMC genomics · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Recent advances in the characterization of essential genes and development of a database of essential genes.iMeta · 2024Review
- Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification.Frontiers in genetics · 2023Article
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- Bacterial genome reductions: Tools, applications, and challenges.Frontiers in genome editing · 2022Review
- DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction.BMC bioinformatics · 2021Article
- NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks.Frontiers in genetics · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- DeeplyEssential: a deep neural network for predicting essential genes in microbes.BMC bioinformatics · 2020Article
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
- An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.Computational and structural biotechnology journal · 2019Article
- Identifying mouse developmental essential genes using machine learning.Disease models & mechanisms · 2018Article
- Network-based features enable prediction of essential genes across diverse organisms.PloS one · 2018Article
- Sequence-based information-theoretic features for gene essentiality prediction.BMC bioinformatics · 2017Article
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5 authors.
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Abstract
Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.
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