ArticleNAR genomics and bioinformatics2021
Identifying essential genes across eukaryotes by machine learning.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction.bioRxiv : the preprint server for biology · 2026Article
- A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.Genome research · 2026Article
- A pan-viral map of host dependency factors from multi-omics integration and machine learning across influenza A, SARS-CoV-2, Zika, and dengue viruses.Journal of translational medicine · 2026Article
- Pathways, outputs and impact of NIH-supported bioinformatics and genomics graduate trainees in Africa.Briefings in bioinformatics · 2026Review
- Drug toxicity prediction based on genotype-phenotype differences between preclinical models and humans.EBioMedicine · 2025Article
- Superior target genes and pathways for RNAi-mediated pest control revealed by genome-wide analysis in the beetle Tribolium castaneum.Pest management science · 2025Article
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- Application of machine learning and genomics for orphan crop improvement.Nature communications · 2025Review
- Inference of essential genes inComputational and structural biotechnology journal · 2024Article
- HELP: A computational framework for labelling and predicting human common and context-specific essential genes.PLoS computational biology · 2024Article
- Inference of Essential Genes of the ParasiteInternational journal of molecular sciences · 2024Article
- Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.PloS one · 2024Article
- Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.Biomolecules · 2023Article
- 'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.Briefings in bioinformatics · 2023Article
- Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral.Frontiers in microbiology · 2023Article
- Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica.Nature communications · 2022Article
- Establishing RNAi for basic research and pest control and identification of the most efficient target genes for pest control: a brief guide.Frontiers in zoology · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to
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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.