ArticleBriefings in bioinformatics2021
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.
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 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed, 61 citations in OpenAlex.
- Article
- Identification and application of bioparts for plant synthetic biology.Molecules and cells · 2025Review
- DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.Scientific reports · 2025Article
- iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism.PeerJ. Computer science · 2025Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
- Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.Briefings in bioinformatics · 2024Article
- Predmoter-cross-species prediction of plant promoter and enhancer regions.Bioinformatics advances · 2024Article
- Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions.ACS synthetic biology · 2023Article
- CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture.BMC genomics · 2023Article
- 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
- CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins.Computational and structural biotechnology journal · 2023Article
- iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.BMC genomics · 2022Article
- Database of Potential Promoter Sequences in theBiology · 2022Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization ofFrontiers in genetics · 2022Article
- Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.Frontiers in pharmacology · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
Abstract
A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core promoter is essential for our understanding of the transcriptional regulation of genes. As a complement to conventional experimental methods, computational techniques with easy-to-use platforms as essential bioinformatics tools can be effectively applied to annotate the functions and physiological roles of promoters. In this work, we propose a deep learning-based method termed Depicter (Deep learning for predicting promoter), for identifying three specific types of promoters, i.e. promoter sequences with the TATA-box (TATA model), promoter sequences without the TATA-box (non-TATA model), and indistinguishable promoters (TATA and non-TATA model). Depicter is developed based on an up-to-date, species-specific dataset which includes Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana promoters. A convolutional neural network coupled with capsule layers is proposed to train and optimize the prediction model of Depicter. Extensive benchmarking and independent tests demonstrate that Depicter achieves an improved predictive performance compared with several state-of-the-art methods. The webserver of Depicter is implemented and freely accessible at https://depicter.erc.monash.edu/.
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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.