ArticleProceedings of the National Academy of Sciences of the United States of America2019
Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
What it found
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Who cites it
86 citing papers in PubMed, 197 citations in OpenAlex.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Genome-Wide Identification of SNF7 Gene Family in Maize and Potential Roles in Response to Abiotic and Biotic Stress.International journal of molecular sciences · 2026Article
- A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.Genome research · 2026Article
- Upstream, downstream: nemo learns where plant regulatory sequences act.Plant physiology · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.Plant biotechnology journal · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- PlantSetDelta: Mining regulatory features to enable interpretable gene set analysis in plants.Plant communications · 2026Article
- Discriminating models of trait evolution.Evolution; international journal of organic evolution · 2026Article
- Deep learning-based semantic matching of cis-regulatory DNA sequences facilitates the prediction of gene function.Nature plants · 2026Article
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Responses and adaptations of plants to abiotic stress: transcriptional regulation of secondary metabolic pathways, metabolomics, and nanobiological approaches.Frontiers in genetics · 2026Review
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Investigating cis-regulatory elements and gene expression in multiple tomato varieties using interpretable deep learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Precise engineering of gene expression by editing plasticity.Genome biology · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (
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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.