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

• Published in: Scientific reports Vol. 13; no. 1; pp. 15899 - 12
• Main Authors: Nazari, Leyla, Aslan, Muhammet Fatih, Sabanci, Kadir, Ropelewska, Ewa
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.09.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/337; 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

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....