ArticleBMC systems biology2018
70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 91 citations in OpenAlex.
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 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
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model.BMC bioinformatics · 2024Article
- CircSI-SSL: circRNA-binding site identification based on self-supervised learning.Bioinformatics (Oxford, England) · 2024Article
- iProm-Sigma54: A CNN Base Prediction Tool forCells · 2023Article
- ProkBERT family: genomic language models for microbiome applications.Frontiers in microbiology · 2023Article
- Article
- Genome-wide promoter assembly inGenome research · 2022Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- SAPPHIRE.CNN: Implementation of dRNA-seq-driven, species-specific promoter prediction using convolutional neural networks.Computational and structural biotechnology journal · 2022Article
- TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization ofFrontiers in genetics · 2022Article
- Sigma70Pred: A highly accurate method for predicting sigma70 promoter inFrontiers in microbiology · 2022Article
- Accurate identification of RNA D modification using multiple features.RNA biology · 2021Article
- Promotech: a general tool for bacterial promoter recognition.Genome biology · 2021Article
- DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.Briefings in bioinformatics · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm.Frontiers in genetics · 2021Review
- iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPromoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ
resultsIn order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNP
conclusionThe jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/ .
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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.