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

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dc.authoridnazari, leyla/0000-0003-0940-8486
dc.authoridRopelewska, Ewa/0000-0001-8891-236X
dc.authoridASLAN, Muhammet Fatih/0000-0001-7549-0137
dc.contributor.authorNazari, Leyla
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorSabanci, Kadir
dc.contributor.authorRopelewska, Ewa
dc.date.accessioned2024-01-22T12:22:18Z
dc.date.available2024-01-22T12:22:18Z
dc.date.issued2023
dc.departmentKMÜen_US
dc.description.abstractBiotic 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.en_US
dc.identifier.doi10.1038/s41598-023-42984-4
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid37741865en_US
dc.identifier.pmid37741865
dc.identifier.scopus2-s2.0-85171972331
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-023-42984-4
dc.identifier.urihttps://hdl.handle.net/11492/7896
dc.identifier.volume13en_US
dc.identifier.wosWOS:001080555400026
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolioen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzkmusnmz
dc.titleIntegrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stressen_US
dc.typeArticle

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