ArticleBMC systems biology2010
Identifying essential genes in bacterial metabolic networks with machine learning methods.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 125 citations in OpenAlex.
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- Profiling of Key Hub Genes Using a Two-State Weighted Gene Co-Expression Network of 'Jao Khao' Rice under Soil Salinity Stress Based on Time-Series Transcriptome Data.International journal of molecular sciences · 2024Article
- Joint global and local interpretation method for CIN status classification in breast cancer.Heliyon · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- Article
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- Biological network topology features predict gene dependencies in cancer cell-lines.Bioinformatics advances · 2022Article
- Review
- Bacterial genome reductions: Tools, applications, and challenges.Frontiers in genome editing · 2022Review
- Identifying essential genes across eukaryotes by machine learning.NAR genomics and bioinformatics · 2021Article
- DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction.BMC bioinformatics · 2021Article
- Prediction of Human-Bioinformatics and biology insights · 2021Article
- 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
- Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells.Molecules (Basel, Switzerland) · 2020Article
- Heterogeneous Network Model to Identify Potential Associations BetweenInternational journal of molecular sciences · 2020Article
- Prediction of essential genes in prokaryote based on artificial neural network.Genes & genomics · 2020Article
- Essential gene prediction inComputational and structural biotechnology journal · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.
resultsWe developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.
conclusionsUsing elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.
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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.