ArticleBMC bioinformatics2020
DeeplyEssential: a deep neural network for predicting essential genes in microbes.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 34 citations in OpenAlex.
- 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
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- A deep ensemble framework for human essential gene prediction by integrating multi-omics data.Scientific reports · 2025Article
- A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.BMC biology · 2025Article
- Genome engineering on size reduction and complexity simplification: A review.Journal of advanced research · 2024Review
- Recent advances in the characterization of essential genes and development of a database of essential genes.iMeta · 2024Review
- An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.BMC bioinformatics · 2023Article
- DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.Bioinformatics (Oxford, England) · 2023Article
- Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica.Nature communications · 2022Article
- Review
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- Bacterial genome reductions: Tools, applications, and challenges.Frontiers in genome editing · 2022Review
- Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves.Sensors (Basel, Switzerland) · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- Selected Research Articles from the 2019 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC).BMC genomics · 2020Article
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
- Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.PloS one · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEssential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies.
resultsWe propose a deep neural network for predicting essential genes in microbes. Our architecture called DEEPLYESSENTIAL makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DEEPLYESSENTIAL outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes.
conclusionDeep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.