Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows

Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows

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We construct two meta-objectives using expert annotations

17 of 20

17 of 20

We construct two meta-objectives using expert annotations

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Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows

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  1. 1 Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows
  2. 2 Overview
  3. 3 Bayesian optimization in bioinformatics has been applied to protein design
  4. 4 Most bioinformatic analyses are unsupervised
  5. 5 A typical workflow involves many steps and parameters
  6. 6 Difficulty with defining objectives make AutoML challenging to apply in bioinformatics
  7. 7 AutoML approaches construct objectives for a given problem
  8. 8 Motivation for AutoGeneS constructed objectives
  9. 9 Our method automatically infers which objectives are useful to guide optimization
  10. 10 MOBO basics
  11. 11 We build on the random scalarizations approach that returns a subset of the Pareto front
  12. 12 We determine the region of the Pareto front using objective behaviours
  13. 13 Three examples of desirable behaviours
  14. 14 Toy data simulating useful and not useful objectives
  15. 15 Optimizing cofactor in the analysis of Imaging Mass Cytometry (IMC) data
  16. 16 We construct objectives for clustering workflow using pairs of co-expressed proteins
  17. 17 We construct two meta-objectives using expert annotations
  18. 18 Parameters selected by our method led to clusterings that agree with expert annotations
  19. 19 Quantitative evaluation of performance
  20. 20 Summary

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