ArticleNucleic acids research2011
Investigating the predictability of essential genes across distantly related organisms using an integrative approach.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 58 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it, 134 citations in OpenAlex.
- Meta-analysis of dispensable essential genes and their interactions with bypass suppressors.Life science alliance · 2024Pooled it
- Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.Journal of proteomics · 2025Article
- Subtractive genomics and drug repurposing strategies for targetingFrontiers in microbiology · 2025Article
- Impact of fleQ Deficiency on Resource Allocation and Heterologous Gene Expression in Pseudomonas putida Across Various Growth Media.Microbial biotechnology · 2024Article
- Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- 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
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- Bacterial genome reductions: Tools, applications, and challenges.Frontiers in genome editing · 2022Review
- Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis.PloS one · 2022Article
- DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction.BMC bioinformatics · 2021Article
- A novel essential protein identification method based on PPI networks and gene expression data.BMC bioinformatics · 2021Article
- Comparative Metabolic Pathways Analysis and Subtractive Genomics Profiling to Prioritize Potential Drug Targets AgainstFrontiers in microbiology · 2021Article
- Research on the Computational Prediction of Essential Genes.Frontiers in cell and developmental biology · 2021Review
- 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
- Essential gene prediction inComputational and structural biotechnology journal · 2020Article
- Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.PloS one · 2020Article
- Prediction of essential genes in prokaryote based on artificial neural network.Genes & genomics · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 2 institutions in 3 countries.
Funding
Abstract
Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.
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What OpenQuestion holds
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.