ReviewJournal of integrative plant biology2025
Fast-forwarding plant breeding with deep learning-based genomic prediction.
Review in Journal of integrative plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Harnessing genomics approaches for heat stress resilience in wheat: from discovery to deployment.Journal of experimental botany · 2026Review
- Melatonin seed priming: A climate-smart, green strategy to enhance abiotic stress tolerance in plants.Journal of integrative plant biology · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- Article
- Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025Review
- Review
- Fast-forwarding plant breeding with deep learning-based genomic prediction.Journal of integrative plant biology · 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
7 authors.
Funding
Abstract
Deep learning-based genomic prediction (DL-based GP) has shown promising performance compared to traditional GP methods in plant breeding, particularly in handling large, complex multi-omics data sets. However, the effective development and widespread adoption of DL-based GP still face substantial challenges, including the need for large, high-quality data sets, inconsistencies in performance benchmarking, and the integration of environmental factors. Here, we summarize the key obstacles impeding the development of DL-based GP models and propose future developing directions, such as modular approaches, data augmentation, and advanced attention mechanisms.
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.