ArticleBMC bioinformatics2009
Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- A machine learning classifier to identify and prioritise genes associated with murine cardiac development.PLoS genetics · 2026Article
- A deep learning model for predicting essential proteins based on an attention mechanism.Frontiers in genetics · 2026Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.Health information science and systems · 2023Article
- An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.BMC bioinformatics · 2023Article
- Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins.PloS one · 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
- A disease-related essential protein prediction model based on the transfer neural network.Frontiers in genetics · 2022Article
- Bacterial genome reductions: Tools, applications, and challenges.Frontiers in genome editing · 2022Review
- Review
- 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
- Characterization of the Survival Influential Genes in Carcinogenesis.International journal of molecular sciences · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- Article
- DeeplyEssential: a deep neural network for predicting essential genes in microbes.BMC bioinformatics · 2020Article
- Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.Genes · 2020Article
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- Essential gene prediction inComputational and structural biotechnology journal · 2020Article
- DeepEP: a deep learning framework for identifying essential proteins.BMC bioinformatics · 2019Article
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Authors and funding
2 authors.
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Abstract
backgroundThe identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.
resultsWe constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.
conclusionWe were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.
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