Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress

Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize....

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Bibliographic Details
Published in:Scientific reports 2023-09, Vol.13 (1), p.15899-15899, Article 15899
Main Authors: Nazari, Leyla, Aslan, Muhammet Fatih, Sabanci, Kadir, Ropelewska, Ewa
Format: Article
Language:English
Subjects:
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631/449
639/705
Algorithms
Artificial intelligence
Corn
Crop production
Cultivars
Deep learning
Disease resistance
Feature selection
Gene expression
Genes
Humanities and Social Sciences
Learning algorithms
Long short-term memory
Machine learning
Meta-analysis
multidisciplinary
Neural networks
Polyphenol oxidase
Science
Science (multidisciplinary)
Transcriptomics
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Summary:Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes (( S )- beta-macrocarpene synthase , zealexin A1 synthase , polyphenol oxidase I , chloroplastic , pathogenesis-related protein 10 , CHY1 , chitinase chem 5 , barwin , and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-42984-4