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| Hlavní autor: | |
|---|---|
| Médium: | Recurso digital |
| Jazyk: | angličtina |
| Vydáno: |
Zenodo
2026
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| Témata: | |
| On-line přístup: | https://doi.org/10.5281/zenodo.19659082 |
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- This comprehensive technical guide examines the complex paradigm of plant genotype-to-phenotype (G2P) relationships, a critical area of research for accelerating crop improvement and advancing plant-based drug discovery. The article begins by outlining the fundamental mechanisms of genetic variation, including single nucleotide polymorphisms, insertion-deletion polymorphisms, presence-absence variations, and the pervasive impacts of epistasis and genetic redundancy. It emphasizes that phenotypic expression is heavily dictated by genotype-by-environment (GxE) interactions, necessitating robust multi-environment trials and advanced environmental covariate tracking (envirotyping) to accurately isolate genetic signals from environmental noise. A major focus of the text is the evolution of computational frameworks used for genomic prediction. Traditional linear models, such as Genomic Best Linear Unbiased Prediction (GBLUP), are contrasted with advanced machine learning and deep learning architectures, including Convolutional Neural Networks, Transformers, and ensemble methods like the EXGEP framework. These modern approaches are shown to better capture the non-linear, complex genetic architectures inherent in polygenic traits. The guide also highlights the indispensable role of high-throughput phenotyping platforms, utilizing RGB, hyperspectral, and 3D sensors, to generate the massive datasets required to train these sophisticated models. Furthermore, the article explores the integration of multi-omics data (genomics, transcriptomics, proteomics, metabolomics) to provide a systems-level understanding of plant biology. It details the application of G2P research in phenotypic drug discovery, where plant genetic diversity is leveraged to identify novel phytopharmaceuticals without prior knowledge of molecular targets. To ensure the reliability of predictive models, the guide outlines rigorous experimental protocols for data standardization, utilizing frameworks like MIAPPE, and robust validation strategies, including K-fold and Leave-One-Field-Out cross-validation. Finally, it discusses the implementation of Explainable AI (XAI), such as SHAP values, to translate black-box model predictions into biologically interpretable insights, ultimately empowering researchers and breeders to make informed decisions for sustainable agriculture and pharmaceutical innovation. Source: https://www.plantbiosci.com/posts/from-code-to-crop-decoding-plant-genotypetophenotype-relationships-for-advanced-research-and-drug-discovery