ArticleBMC bioinformatics2017
Sequence-based information-theoretic features for gene essentiality prediction.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 50 citations in OpenAlex.
- A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction.bioRxiv : the preprint server for biology · 2026Article
- CNN4Essential: a convolutional neural network model for predicting bacterial gene essentiality based on multi-feature fusion.BMC genomics · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- In silico characterization, structural modeling, and molecular docking of GabP in citrus and its potential role in GABA uptake.Scientific reports · 2025Article
- 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
- Recent advances in genetic tools for engineering probiotic lactic acid bacteria.Bioscience reports · 2023Article
- Evaluation of machine learning classifiers for predicting essential genes inBioinformation · 2022Article
- DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction.BMC bioinformatics · 2021Article
- On the relation of gene essentiality to intron structure: a computational and deep learning approach.Life science alliance · 2021Article
- IIMLP: integrated information-entropy-based method for LncRNA prediction.BMC bioinformatics · 2021Article
- NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks.Frontiers in genetics · 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
- 6mA-RicePred: A Method for Identifying DNAFrontiers in plant science · 2020Article
- Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.PloS one · 2020Article
- A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.BMC bioinformatics · 2019Article
- An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.Computational and structural biotechnology journal · 2019Article
- Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes.Frontiers in microbiology · 2019Article
- Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.
resultsWe developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).
conclusionsThe proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.
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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.