ArticleBioinformatics (Oxford, England)2017
Accurate prediction of human essential genes using only nucleotide composition and association information.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 72 citations in OpenAlex.
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- A deep ensemble framework for human essential gene prediction by integrating multi-omics data.Scientific reports · 2025Article
- EssSubgraph improves performance and generalizability of mammalian essential gene prediction with large networks.GigaScience · 2025Article
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- Recent advances in the characterization of essential genes and development of a database of essential genes.iMeta · 2024Review
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.PloS one · 2024Article
- ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plants.Functional & integrative genomics · 2023Article
- ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants.Functional & integrative genomics · 2023Article
- DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.Bioinformatics (Oxford, England) · 2023Article
- Consistent Clustering Pattern of Prokaryotic Genes Based on Base Frequency at the Second Codon Position and its Association with Functional Category Preference.Interdisciplinary sciences, computational life sciences · 2022Article
- Identifying essential genes across eukaryotes by machine learning.NAR genomics and bioinformatics · 2021Article
- Use of Average Mutual Information and Derived Measures to Find Coding Regions.Entropy (Basel, Switzerland) · 2021Article
- On the relation of gene essentiality to intron structure: a computational and deep learning approach.Life science alliance · 2021Article
- OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines.Nucleic acids research · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
- Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.Genes · 2020Article
- Essential gene prediction inComputational and structural biotechnology journal · 2020Article
- Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.Biomolecules · 2019Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPreviously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.
resultsOur model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements. AVAILABILITY AND IMPLEMENTATION: http://cefg.uestc.edu.cn/Pheg. CONTACT: fbguo@uestc.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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