Continuous Valued Phenotype

Continuous-valued phenotypes, representing complex traits with a range of values (e.g., height, blood pressure), are increasingly studied using machine learning to predict them from genetic or clinical data. Current research focuses on improving the accuracy of phenotype extraction from diverse data sources like electronic health records and biomedical literature, employing techniques like large language models (LLMs), tensor factorization, and unsupervised ensemble methods to identify meaningful patterns and build predictive models. These advancements are crucial for enhancing our understanding of complex diseases, improving diagnostic accuracy, and developing personalized medicine approaches.