ArticleFrontiers in bioengineering and biotechnology2019
Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 129 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 2026Article
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- NetStart 2.0: prediction of eukaryotic translation initiation sites using a protein language model.BMC bioinformatics · 2025Article
- Pathogenic variants prevalence patients with diabetic kidney disease in Japan: A descriptive study.Journal of diabetes investigation · 2025Article
- Beyond small molecules: advancing MYC-targeted cancer therapies through protein engineering.Transcription · 2025Review
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- DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.Scientific reports · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- 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
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
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- Mapping the epigenomic landscape of post-traumatic stress disorder in human cortical neurons.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation.Genome biology · 2024Article
- iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model.BMC bioinformatics · 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
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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
A promoter is a short region of DNA (100-1,000 bp) where transcription of a gene by RNA polymerase begins. It is typically located directly upstream or at the 5' end of the transcription initiation site. DNA promoter has been proven to be the primary cause of many human diseases, especially diabetes, cancer, or Huntington's disease. Therefore, classifying promoters has become an interesting problem and it has attracted the attention of a lot of researchers in the bioinformatics field. There were a variety of studies conducted to resolve this problem, however, their performance results still require further improvement. In this study, we will present an innovative approach by interpreting DNA sequences as a combination of continuous FastText N-grams, which are then fed into a deep neural network in order to classify them. Our approach is able to attain a cross-validation accuracy of 85.41 and 73.1% in the two layers, respectively. Our results outperformed the state-of-the-art methods on the same dataset, especially in the second layer (strength classification). Throughout this study, promoter regions could be identified with high accuracy and it provides analysis for further biological research as well as precision medicine. In addition, this study opens new paths for the natural language processing application in omics data in general and DNA sequences in particular.
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