ReviewFrontiers in molecular biosciences2021
Learning the Regulatory Code of Gene Expression.
Review in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks.PLoS computational biology · 2026Article
- Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- A comparative study of flaxseed gum effect on Lactobacillus acidophilus genes expression, and textural, sensory, structural, and microbiological properties of synbiotic Iranian white cheese.Scientific reports · 2025Article
- Learning the sequence code of protein expression in human immune cells.Science advances · 2025Article
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- BAC-browser: the tool for synthetic biology.BMC bioinformatics · 2025Article
- Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models.Frontiers in microbiology · 2025Article
- UTRGAN: learning to generate 5' UTR sequences for optimized translation efficiency and gene expression.Bioinformatics advances · 2025Article
- Strategies for effectively modelling promoter-driven gene expression using transfer learning.bioRxiv : the preprint server for biology · 2024Article
- Promoters in Pichia pastoris: A Toolbox for Fine-Tuned Gene Expression.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.Plant communications · 2023Review
- Effective design and inference for cell sorting and sequencing based massively parallel reporter assays.Bioinformatics (Oxford, England) · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice.Frontiers in plant science · 2023Article
- The potential of cold-shock promoters for the expression of recombinant proteins in microbes and mammalian cells.Journal, genetic engineering & biotechnology · 2022Review
- Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.PLoS computational biology · 2022Article
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the
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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.