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|a 10.1111/nph.20264
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Sia, Jayson
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Machine learning-based identification of general transcriptional predictors for plant disease
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|c 2025
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 18.12.2024
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|a Date Revised 18.12.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.
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|a This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine learning models were trained to predict disease development from early transcriptional responses. Feature selection techniques based on network science and topology were used to train models employing only a fraction of the transcriptome. Machine learning models trained on one pathosystem where then validated by predicting disease development in new pathosystems. The identified feature selection gene sets were enriched for pathways related to biotic, abiotic, and stress responses, though the specific genes involved differed between feature sets. This suggests common immune responses to diverse pathogens that operate via different gene sets. The study demonstrates that machine learning can uncover both established and novel components of the plant's immune response, offering insights into disease resistance mechanisms. These predictive models highlight the potential to advance our understanding of multigenic outcomes in plant immunity and can be further refined for applications in disease prediction
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Arabidopsis thaliana
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| 650 |
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4 |
|a feature selection
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| 650 |
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4 |
|a general stress response
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a network science
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| 650 |
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4 |
|a plant–pathogen interaction
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| 650 |
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4 |
|a predictive biology
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| 700 |
1 |
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|a Zhang, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheng, Mingxi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bogdan, Paul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cook, David E
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t The New phytologist
|d 1979
|g 245(2024), 2 vom: 22. Jan., Seite 785-806
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|x 1469-8137
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|g volume:245
|g year:2024
|g number:2
|g day:22
|g month:01
|g pages:785-806
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|u http://dx.doi.org/10.1111/nph.20264
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