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Biolearn
  • Quickstart
  • Clocks and Other Models
  • GEO Data Sources
  • DNA Methylation Array Data Standard V-2410
  • Examples
    • Omics Biomarker Examples
      • Local Data Loading
      • “Transcriptomic Clock” using GEO Data
      • “Epigenetic Clocks” in GEO Data
      • Down Syndrome Epigenetic Plotting
      • Performing custom imputations
    • Composite Biomarkers Examples
      • “Phenotypic Ages” in NHANES Data
    • Challenge Submission Examples
      • Building a competition submission using an existing model
      • Exploring the Challenge Data
      • Training an ElasticNet model
    • Deconvolution Examples
      • “Deconvolution Example”
    • Additional Visualization Examples
      • Quality control visualization using GEO datasets
      • Clock/model visualizations using GEO datasets
      • DNA methylation visualizations using GEO datasets
  • API References
    • biolearn.data_library: DataLibrary
      • biolearn.data_library.DataLibrary
      • biolearn.data_library.DataSource
      • biolearn.data_library.GeoData
    • biolearn.model_gallery: ModelGallery
      • biolearn.model_gallery.ModelGallery
    • biolearn.cache: Cache
      • biolearn.cache.NoCache
      • biolearn.cache.LocalFolderCache
    • biolearn.imputation: Data Imputation Functions
      • biolearn.imputation.impute_from_standard
      • biolearn.imputation.impute_from_average
      • biolearn.imputation.hybrid_impute
    • biolearn.mortality: Mortality Predictor Evaluation
      • biolearn.mortality.run_predictions
      • biolearn.mortality.calculate_c_index
      • biolearn.mortality.calculate_mortality_hazard_ratios
      • biolearn.mortality.calculate_age_adjusted_c_index
      • biolearn.mortality.calculate_log_rank_test
      • biolearn.mortality.plot_hazard_ratios
    • biolearn.load: Data Loading Utilities
      • biolearn.load.load_nhanes
      • biolearn.load.load_fhs

Development

  • Team
  • GitHub Repository
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biolearn.model_gallery: ModelGallery#

This module provides implementations of different biomarker models and clocks

Classes:

ModelGallery([models])

ModelGallery manages a collection of Models that can be run on biological data to produce predictions.

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biolearn.model_gallery.ModelGallery
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