PLOS
open access
CC BY 4.0
LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.#236356
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.#258457
- QTL mapping, validation, and candidate gene predicting for maize plant architecture traits via bulked segregant analysis plus linkage analysis with F₂ population.#255520
- Optimisation of Weighted Ensembles of Genomic Prediction Models in Maize#151328
- Optimisation of Weighted Ensembles of Genomic Prediction Models in Maize#148836
Record · ID 209845
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive