ArticleBriefings in bioinformatics2021
A survey on deep learning in DNA/RNA motif mining.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Extracellular Vesicles: Classification, Biological Functions, Diseases, and Therapeutic Opportunities.MedComm · 2026Review
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- CNNKSCEC: a deep learning-based framework for chromatin loop prediction with multi-source feature integration.Frontiers in genetics · 2026Article
- Predict SARS-CoV-2 genome interactions based on RNA language models.Biosafety and health · 2025Article
- AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.Current allergy and asthma reports · 2025Review
- GINClus: RNA structural motif clustering using graph isomorphism network.NAR genomics and bioinformatics · 2025Article
- EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.BMC genomics · 2024Article
- DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model.Computational and structural biotechnology journal · 2024Article
- Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.Bioinformatics (Oxford, England) · 2024Article
- DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.Briefings in bioinformatics · 2024Article
- BIOMAPP::CHIP: large-scale motif analysis.BMC bioinformatics · 2024Article
- Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.BMC genomics · 2023Article
- KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.Journal of translational medicine · 2023Article
- DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.Briefings in bioinformatics · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.Briefings in bioinformatics · 2023Article
- TSPTFBS 2.0: trans-species prediction of transcription factor binding sites and identification of their core motifs in plants.Frontiers in plant science · 2023Article
- Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks.Frontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN-RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.
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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 Socratic graph.