ReviewaBIOTECH2024
Integrating machine learning and genome editing for crop improvement.
Review in aBIOTECH, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Mathematical modeling with RGB data for comparative analysis of rice lineages and distribution of breaking points.BMC plant biology · 2026Article
- AI-Guided DNA-Free and Genotype-Independent Genome Editing for Soybean Improvement.Plants (Basel, Switzerland) · 2026Review
- Beyond CRISPR/Cas9: emerging genome editing technologies for next-generation crop improvement.Molecular biology reports · 2026Review
- Review
- Advances in luminescence-based tracing technologies: applications and future perspectives.Biodesign research · 2026Review
- Epigenome and interactome profiling uncovers principles of distal regulation in the barley genome.Cell genomics · 2026Article
- AI-assisted crop improvement: new design tools within established regulatory frameworks.Frontiers in plant science · 2026Article
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Revolution and advances in gene editing and genomics technology for developing climate-resilient legume crops: developments and prospects.Plant molecular biology · 2025Review
- Smart agriculture in Asia.Plant communications · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Article
- A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes.Plants (Basel, Switzerland) · 2025Review
- Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model.Frontiers in plant science · 2025Article
- Review
- Recent advances of CRISPR-based genome editing for enhancing staple crops.Frontiers in plant science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.