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Unsupervised encoding selection through ensemble pruning for biomedical classification.

Sebastian SpänigAlexander MichelDominik Heider
Published in: BioData mining (2023)
The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain.
Keyphrases
  • machine learning
  • convolutional neural network
  • electronic health record
  • neural network
  • deep learning
  • wastewater treatment