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doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows.

Daniel SvenssonRickard SjögrenDavid SundellAndreas SjödinJohan Trygg
Published in: BMC bioinformatics (2019)
Our proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.
Keyphrases
  • healthcare
  • primary care
  • magnetic resonance
  • deep learning
  • electronic health record
  • machine learning
  • data analysis
  • big data
  • quality improvement
  • contrast enhanced
  • neural network