ArticleFrontiers in genetics2022
TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Unveiling the risks of ChatGPT in diagnostic surgical pathology.Virchows Archiv : an international journal of pathology · 2025Article
- Review
- iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism.PeerJ. Computer science · 2025Article
- An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss.PeerJ. Computer science · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.Briefings in bioinformatics · 2024Article
- Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges.Ophthalmology science · 2023Article
- Large language models in medicine.Nature medicine · 2023Review
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the introduction of the first transformer model with a unique self-attention mechanism, natural language processing (NLP) models have attained state-of-the-art (SOTA) performance on various tasks. As DNA is the blueprint of life, it can be viewed as an unusual language, with its characteristic lexicon and grammar. Therefore, NLP models may provide insights into the meaning of the sequential structure of DNA. In the current study, we employed and compared the performance of popular SOTA NLP models (i.e., XLNET, BERT, and a variant DNABERT trained on the human genome) to predict and analyze the promoters in freshwater cyanobacterium
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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.