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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2404.15776 |
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| _version_ | 1866913467631403008 |
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| author | Koh, Eugene Sunil, Rohan Shawn Lam, Hilbert Yuen In Mutwil, Marek |
| author_facet | Koh, Eugene Sunil, Rohan Shawn Lam, Hilbert Yuen In Mutwil, Marek |
| contents | Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing Big Data and Artificial Intelligence to Study Plant Stress Koh, Eugene Sunil, Rohan Shawn Lam, Hilbert Yuen In Mutwil, Marek Quantitative Methods Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants. |
| title | Harnessing Big Data and Artificial Intelligence to Study Plant Stress |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2404.15776 |