ArticleNucleic acids research2022
iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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52 citing papers in PubMed, 88 citations in OpenAlex.
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- PBP_ICBA: a prediction of bacterial promoters in specific organisms using an improved convolutional block attention module.Journal of computer-aided molecular design · 2026Article
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
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- Benchmarking DNA foundation models for genomic and genetic tasks.Nature communications · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.International journal of molecular sciences · 2025Article
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- Intronomics-MIP: a snakemake pipeline for analyzing multilocus intron polymorphisms in species identification and population genomics.BMC research notes · 2025Article
- A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.Briefings in bioinformatics · 2025Article
- Modeling within-host Chikungunya virus dynamics with the immune system using semi-analytical approaches.BMC research notes · 2025Article
- Assessing the effectiveness of group motivational interviewing in raising awareness of mobile gaming addiction among medical students: a pilot study.BMC research notes · 2025Article
- Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Promoters are consensus DNA sequences located near the transcription start sites and they play an important role in transcription initiation. Due to their importance in biological processes, the identification of promoters is significantly important for characterizing the expression of the genes. Numerous computational methods have been proposed to predict promoters. However, it is difficult for these methods to achieve satisfactory performance in multiple species. In this study, we propose a novel weighted average ensemble learning model, termed iPro-WAEL, for identifying promoters in multiple species, including Human, Mouse, E.coli, Arabidopsis, B.amyloliquefaciens, B.subtilis and R.capsulatus. Extensive benchmarking experiments illustrate that iPro-WAEL has optimal performance and is superior to the current methods in promoter prediction. The experimental results also demonstrate a satisfactory prediction ability of iPro-WAEL on cross-cell lines, promoters annotated by other methods and distinguishing between promoters and enhancers. Moreover, we identify the most important transcription factor binding site (TFBS) motif in promoter regions to facilitate the study of identifying important motifs in the promoter regions. The source code of iPro-WAEL is freely available at https://github.com/HaoWuLab-Bioinformatics/iPro-WAEL.
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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.