ArticleMolecular therapy. Nucleic acids2019
iProEP: A Computational Predictor for Predicting Promoter.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- Designing and testing CRISPRi-based synthetic gene circuits in plants.Nature protocols · 2026Review
- Retrotransposed gene copies persist under relaxed selection in wild Spodoptera frugiperda.Molecular biology and evolution · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- Characterization of archaeal promoters using explainable and web-based CNN model.BMC genomics · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- mRNA initiation and termination are spatially coordinated.Science (New York, N.Y.) · 2025Article
- Bioinformatic exploration reveals features of tenpIN family of type III toxin-antitoxin systems in bacteria and viruses.Scientific reports · 2025Article
- U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation.Molecular cell · 2025Article
- Harnessing promoter elements to enhance gene editing in plants: perspectives and advances.Plant biotechnology journal · 2025Review
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- Quantitative mapping of pseudouridines in bacteria RNA.bioRxiv : the preprint server for biology · 2024Article
- Ethylene and epoxyethane metabolism in methanotrophic bacteria: comparative genomics and physiological studies usingMicrobial genomics · 2024Article
- CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.bioRxiv : the preprint server for biology · 2024Article
- Making a Pathogen? Evaluating the Impact of Protist Predation on the Evolution of Virulence in Serratia marcescens.Genome biology and evolution · 2024Article
- msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.BMC biology · 2024Article
- Computationally guided AAV engineering for enhanced gene delivery.Trends in biochemical sciences · 2024Review
- iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.Heliyon · 2024Article
- CDBProm: the Comprehensive Directory of Bacterial Promoters.NAR genomics and bioinformatics · 2024Article
- Regulatory Element Analysis and Comparative Genomics Study of Heavy Metal-Resistant Genes in the Complete GenomeBioinformatics and biology insights · 2024Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).
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Registered trials
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.