ArticleFrontiers in genetics2019
DeePromoter: Robust Promoter Predictor Using Deep Learning.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed, 196 citations in OpenAlex.
- UnconditionalSynthetic and systems biotechnology · 2026Article
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 2026Article
- A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest.Genome research · 2026Review
- Optimizing genomic language models for promoter prediction: a comparative study of tokenization and cross-species learning.NAR genomics and bioinformatics · 2026Article
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 2026Review
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Advancements and prospects in key technologies for robotic pollination in greenhouse pepper breeding: a review.Frontiers in plant science · 2026Review
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- Annotating the genome at single-nucleotide resolution with DNA foundation models.Nature methods · 2025Article
- Characterization of archaeal promoters using explainable and web-based CNN model.BMC genomics · 2025Article
- Article
- Identification and application of bioparts for plant synthetic biology.Molecules and cells · 2025Review
- CREATE: cell-type-specific cis-regulatory element identification via discrete embedding.Nature communications · 2025Article
- Kolmogorov-Arnold networks for genomic tasks.Briefings in bioinformatics · 2025Article
- Artificial Intelligence-Based Approaches for AAV Vector Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- mRNA-LM: full-length integrated SLM for mRNA analysis.Nucleic acids research · 2025Article
- DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.Scientific reports · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism.PeerJ. Computer science · 2025Article
- Promoter Region and Regulatory Elements of IGF and VIP Genes Associated With Reproductive Traits in Chicken.International journal of genomics · 2025Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.
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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.