ReviewNature communications2025
Application of machine learning and genomics for orphan crop improvement.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Magnetometry for Agriculture and Animal Systems: From Classical Sensors to Quantum-Enabled Biosensing.Biosensors · 2026Review
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Next-Generation Genotyping: Innovations Driving Plant Genomic Improvement.Life (Basel, Switzerland) · 2026Review
- Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design.Plant communications · 2026Review
- Advances in CRISPR/Cas systems for engineering abiotic stress tolerance in plants: mechanisms and future prospects.Planta · 2026Review
- Printing technologies for monitoring crop health.Nature communications · 2026Review
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025Review
- Revitalizing orphan crops to combat food insecurity.Nature communications · 2025Review
- Molecular Network Analysis in Model and Non-Model Legumes: Challenges in Omics Data Interpretation Across Species, with a Focus onPlants (Basel, Switzerland) · 2025Review
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.Planta · 2025Review
- Breeding vegetables for whitefly resistance: past, present, and future in the AI era.Frontiers in plant science · 2025Review
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
Abstract
Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.