ArticleMolecular genetics and genomics : MGG2019
iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features.
Article in Molecular genetics and genomics : MGG, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 55 citations in OpenAlex.
- PBP_ICBA: a prediction of bacterial promoters in specific organisms using an improved convolutional block attention module.Journal of computer-aided molecular design · 2026Article
- Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.ACS synthetic biology · 2025Article
- Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.Bioinformatics (Oxford, England) · 2025Article
- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
- Study of an Enterococcus faecium strain isolated from an artisanal Mexican cheese, whole-genome sequencing, comparative genomics, and bacteriocin expression.Antonie van Leeuwenhoek · 2024Article
- PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.Genes · 2023Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- Mechanosensitive channel MscS is critical for termination of the bacterial hypoosmotic permeability response.The Journal of general physiology · 2023Article
- ProkBERT family: genomic language models for microbiome applications.Frontiers in microbiology · 2023Article
- TheJournal of microbiology and biotechnology · 2022Article
- Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network.Frontiers in microbiology · 2022Article
- Sigma70Pred: A highly accurate method for predicting sigma70 promoter inFrontiers in microbiology · 2022Article
- TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization ofFrontiers in genetics · 2022Article
- Promotech: a general tool for bacterial promoter recognition.Genome biology · 2021Article
- Explainability in transformer models for functional genomics.Briefings in bioinformatics · 2021Article
- Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.Briefings in bioinformatics · 2021Article
- Article
- Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.Computational and structural biotechnology journal · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In bacterial DNA, there are specific sequences of nucleotides called promoters that can bind to the RNA polymerase. Sigma70 ([Formula: see text]) is one of the most important promoter sequences due to its presence in most of the DNA regulatory functions. In this paper, we identify the most effective and optimal sequence-based features for prediction of [Formula: see text] promoter sequences in a bacterial genome. We used both short-range and long-range DNA sequences in our proposed method. A very small number of effective features are selected from a large number of the extracted features using multi-window of different sizes within the DNA sequences. We call our prediction method iPro70-FMWin and made it freely accessible online via a web application established at http://ipro70.pythonanywhere.com/server for the sake of convenience of the researchers. We have tested our method using a standard benchmark dataset. In the experiments, iPro70-FMWin has achieved an area under the curve of the receiver operating characteristic and accuracy of 0.959 and 90.57%, respectively, which significantly outperforms the state-of-the-art predictors.
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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.