After the highly successful completion of MAZE Phase 1, where a team of researchers gained deep knowledge on doubled-haploid flint lines, Phase 2 officially started in February 2020 and will run for 3 years.
Within the scope of MAZE Phase 2, prediction models based on artificial neural networks (ANN) will be optimized, to extract GxG, GxE and GxGxE interactions. Computomics will contribute with expertise in machine learning-based performance prediction and the extraction of higher-order features that are important for plant performance.
The project is led by Prof. Dr. Chris-Carolin Schön from Technische Universität München.
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