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README.md

README.md

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Notice: The devolpment of this site was ceased. Our new site is: https://github.com/goeckslab/Galaxy-ML. Sorry for the inconvenience caused!

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Galaxy-ML

Galaxy-ML is a web machine learning end-to-end pipeline building framework, with special support to biomedical data. Under the management of unified scikit-learn APIs, cutting-edge machine learning libraries are combined together to provide thousands of different pipelines suitable for various needs. In the form of Galalxy tools, Galaxy-ML provides scalabe, reproducible and transparent machine learning computations.

Key features

  • simple web UI
  • no coding or minimum coding requirement
  • fast model deployment and model selection, specialized in hyperparameter tuning using GridSearchCV
  • high level of parallel and automated computation

Supported modules

A typic machine learning pipeline is composed of a main estimator/model and optional preprocessing component(s).

Model
  • scikit-learn
    • sklearn.ensemble
    • sklearn.linear_model
    • sklearn.naive_bayes
    • sklearn.neighbors
    • sklearn.svm
    • sklearn.tree
  • xgboost
    • XGBClassifier
    • XGBRegressor
  • mlxtend
    • StackingCVClassifier
    • StackingClassifier
    • StackingCVRegressor
    • StackingRegressor
  • keras
    • KerasGClassifier (new API)
    • KerasGRegressor (new API)
Preprocessor
  • scikit-learn
    • sklearn.preprocessing
    • sklearn.feature_selection
    • sklearn.decomposition
    • sklearn.kernel_approximation
    • sklearn.cluster
  • imblanced-learn
    • imblearn.under_sampling
    • imblearn.over_sampling
    • imblearn.combine
  • skrebate
    • ReliefF
    • SURF
    • SURFstar
    • MultiSURF
    • MultiSURFstar
Custom implementations for biomedical application

Examples

  1. Build a simple randomforest model.
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