Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection
Enow Takang Achuo Albert,
Ngalle Hermine Bille,
Bell Joseph Martin
et al.
Abstract:The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine l… Show more
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