Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection
DOI:
https://doi.org/10.25081/jsa.2023.v7.8556Keywords:
Plant disease resistance, Marker-assisted plant selection, Genetic markers, Adiabatic quantum computingAbstract
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 learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed.
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Copyright (c) 2023 Enow Takang Achuo Albert, Ngalle Hermine Bille, Bell Joseph Martin, Ngonkeu Mangaptche Eddy Leonard
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