Structured Multimodal Deep Learning improves Genomic Prediction in Future Environments

Clicks: 5
ID: 324786
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #79 of 317 articles by views in current genetics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 317 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Predicting phenotypes from genetics and environmental inputs is a long-standing challenge in genetics and plant breeding. Deep neural networks are a promising approach due to their capacity to approximate nonlinear biological processes. Despite initial expectations, recent studies have found that deep neural networks under-perform compared to linear methods, even on continent-scale datasets. We attribute this to several failure modes of deep learning, including greedy learning, the tendency to over-emphasize a single type of input data. As a solution, we present the Structured Interaction Neural Network (SINN), which combines statistical decomposition of genetic, environmental and interaction effects with deep neural networks. SINN dissects phenotype prediction into isolated component modeling tasks, revealing poor generalization of learned representations to new environments as the main limitation for prediction of genotype-by-environment interactions and overall yield. We reach competitive performance on yield prediction in the next cycle of 2 maize multi-environment trial datasets, including new genotypes and environments. In the Genomes to Fields (United States) dataset, SINN achieved higher overall accuracy (0.63) than BLUP-based methods (0.43) and a neural network from previous literature (0.48), and surpassed previous top-performing models with a lower RMSE (2.40 vs. 2.46 Mg/ha, mean yield 9.51 Mg/ha). Similar gains were observed in a secondary dataset consisting of Chinese national maize variety trials (MaizeGEP). SINN achieved higher overall accuracy (0.79) than BLUP-based methods (0.49) and a neural network from previous literature (0.76). By combining statistical genetics and modern deep learning, SINN enables accurate, modular and scalable genomic prediction in new environments.
Reference Key
openalex_W4414135326 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aike Potze, Fred A. van Eeuwijk, Ioannis N. Athanasiadis
Journal current genetics
Year 2026
DOI
10.1093/genetics/iyag204
URL
https://doi.org/10.1093/genetics/iyag204
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.