ArticleBioinformatics (Oxford, England)2019
DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Advances in Decoding Bacterial N-Terminal Proteoforms: Technologies, Challenges, and Functional Insights.Microorganisms · 2026Review
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- A foundation model for nucleotide sequences.Nucleic acids research · 2026Article
- NetStart 2.0: prediction of eukaryotic translation initiation sites using a protein language model.BMC bioinformatics · 2025Article
- Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification.Briefings in bioinformatics · 2025Article
- Analysis of RNA translation with a deep learning architecture provides new insight into translation control.Nucleic acids research · 2025Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.Genes · 2024Article
- TIS Transformer: remapping the human proteome using deep learning.NAR genomics and bioinformatics · 2023Article
- A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq.Genomics, proteomics & bioinformatics · 2023Review
- DeepZ: A Deep Learning Approach for Z-DNA Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- Prokaryotic and eukaryotic promoters identification based on residual network transfer learning.Bioprocess and biosystems engineering · 2022Article
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Genomics enters the deep learning era.PeerJ · 2022Review
- ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.PLoS computational biology · 2021Article
- DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BMC bioinformatics · 2021Article
- Mining influential genes based on deep learning.BMC bioinformatics · 2021Article
- Comparison of machine learning and deep learning techniques in promoter prediction across diverse species.PeerJ. Computer science · 2021Article
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4 authors.
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Abstract
motivationRecognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than 'shallow' methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs.
resultsWe developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species. AVAILABILITY AND IMPLEMENTATION: DeepGSR is implemented in Python using Keras API; it is available as open-source software and can be obtained at https://doi.org/10.5281/zenodo.1117159. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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