ReviewFrontiers in artificial intelligence2022
Crop genomic selection with deep learning and environmental data: A survey.
Review in Frontiers in artificial intelligence, 2022. 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.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Bibliometric-Based Analysis of Global Trends and Collaborative Networks in Plant Genetic Engineering (1994-2024).Plant biotechnology journal · 2026Review
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Benchmarking hyperparameter optimization strategies for crop genomic prediction.Frontiers in genetics · 2026Article
- Review
- Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.Plant molecular biology · 2025Review
- Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.Molecular breeding : new strategies in plant improvement · 2025Article
- Obscured-ensemble models for genomic prediction.PloS one · 2025Article
- Omics-Driven Strategies for Developing Saline-Smart Lentils: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.Plant communications · 2024Article
- Stacked generalization as a computational method for the genomic selection.Frontiers in genetics · 2024Article
- The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction.Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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.