CORDIS Project
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This project aims to apply machine learning techniques to analyze phenotypic variation in biological data. It focuses on modeling temporal changes, image structures, and the interrelationships of phenotypic traits, specifically studying Arabidopsis thaliana in collaboration with researchers.
Understanding phenotypic variation, and more particularly identifying the causal genetic or environmental regulators, is a major aim in biological investigations.
The goal of this proposal is to develop and apply machine learning techniques to model key aspects of structure that occur in modern, high-dimensional phenotype datasets.
First, the temporal structure of phenotypes that are recorded over time is addressed.
Statistical models can exploit smoothness of time series and detect change point…
EUROPEAN MOLECULAR BIOLOGY LABORATORY
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