ArticleNature communications2022
Accuracy and data efficiency in deep learning models of protein expression.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 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.
The trial behind it
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
32 citing papers in PubMed.
- Systematic optimization ofEngineering microbiology · 2026Review
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.NAR genomics and bioinformatics · 2026Article
- Transfer learning with pre-trained language models for protein expression level prediction inSynthetic and systems biotechnology · 2026Article
- Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.iScience · 2026Article
- Proteomic signals are not equal clinical phenotypes: redefining evidence standards in arbovirus-SARS-CoV-2 cross-reactivity.Frontiers in immunology · 2026Review
- Probiotic-Based Materials as Living Therapeutics.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Genomic data representations for horizontal gene transfer detection.NAR genomics and bioinformatics · 2025Article
- Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach.Pharmaceutical research · 2025Article
- Enzyme miniaturization: Revolutionizing future biocatalysts.Biotechnology advances · 2025Review
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- Advancing Monoclonal Antibody Manufacturing: Process Optimization, Cost Reduction Strategies, and Emerging Technologies.Biologics : targets & therapy · 2025Review
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor.Scientific reports · 2024Article
- Current limitations in predicting mRNA translation with deep learning models.Genome biology · 2024Article
- The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review.NPJ digital medicine · 2024Review
- Machine learning-assisted substrate binding pocket engineering based on structural information.Briefings in bioinformatics · 2024Article
- Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.Nucleic acids research · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.
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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.