ArticleComputational and structural biotechnology journal2019
An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.
Article in Computational and structural biotechnology journal, 2019. 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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17 citing papers in PubMed, 40 citations in OpenAlex.
- A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction.bioRxiv : the preprint server for biology · 2026Article
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.Molecular genetics and genomics : MGG · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- 'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.Briefings in bioinformatics · 2023Article
- Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm.Frontiers in genetics · 2022Article
- SGII: Systematic Identification of Essential lncRNAs in Mouse and Human Genome With lncRNA-Protein-Protein Heterogeneous Interaction Network.Frontiers in genetics · 2022Article
- Identifying essential genes across eukaryotes by machine learning.NAR genomics and bioinformatics · 2021Article
- Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.International journal of molecular sciences · 2021Article
- High-quality nuclear genome for Sarcoptes scabiei-A critical resource for a neglected parasite.PLoS neglected tropical diseases · 2020Article
- Combined use of feature engineering and machine-learning to predict essential genes inNAR genomics and bioinformatics · 2020Article
- Predicting gene essentiality inComputational and structural biotechnology journal · 2020Article
- Essential gene prediction inComputational and structural biotechnology journal · 2020Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes, and then employed a systematic machine-learning approach, using protein sequence-derived features and selection procedures, to investigate essential gene predictions within and among species. We showed that the numbers of essential gene orthologs comprise small fractions when compared with the total number of orthologs among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trained with subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species. The present work provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes using machine learning approaches.
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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.