Watch a 42-minute AutoML Seminar presentation exploring multi-objective Bayesian optimization techniques for biomedical and molecular data analysis workflows, particularly focusing on unsupervised bioinformatics problems. Learn how to tackle hyperparameter optimization challenges when dealing with undefined objectives by using multiple noisy heuristic metrics. Discover a novel method that infers useful heuristic objectives based on domain-specific criteria and adaptively updates scalarization functions through multi-output Gaussian process surrogate functions. Follow along as the speaker demonstrates practical applications in single-cell RNA sequencing and highly multiplexed imaging datasets, showing how this approach effectively handles clustering analyses where traditional optimization methods struggle. Gain insights into how this method successfully identifies biologically meaningful groupings of cells based on expression profiles, evaluates cluster separation, and validates results against expert annotations in Imaging Mass Cytometry data analysis. Read more