ArticleFrontiers in big data2023
CRMnet: A deep learning model for predicting gene expression from large regulatory sequence datasets.
Article in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- A hybrid 1DCNN-GRU deep learning framework for classifying caprine granulosa cell fertility potential using single-cell transcriptomics.Veterinary world · 2025Article
- Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group AJournal of microbiology and biotechnology · 2025Article
- Untranslated region engineering strategies for gene overexpression, fine-tuning, and dynamic regulation.Journal of microbiology (Seoul, Korea) · 2025Review
- Predicting gene expression using millions of yeast promoters revealsBioinformatics advances · 2025Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimized deep neural network architectures to predict expression from sequences. High predictive performance due to the modeling of dependencies within and between regulatory sequences is an enabler for biological discoveries in gene regulation through model interpretation techniques. To understand the regulatory code that delineates gene expression, we have designed a novel deep-learning model (CRMnet) to predict gene expression in
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Registered trials
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