Feature Ranking for SimBiology

Rank importance of parameters in a SimBiology model for parameter estimation.
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Aggiornato 17 feb 2023

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Feature Ranking
Copyright 2023 The MathWorks, Inc.
Rank importance of parameters in a SimBiology model for parameter estimation. FeatureRanking computes
  • MRMR (minimum redundancy maximum relevance, [1])
  • NCA (neighborhood component analysis, [2])
  • F-TEST based feature ranking.
The ranking scores are computed from parameter samples and associated model simulations. The parameter samples are generated using a SimBiology.Scenarios object, the associated model simulations are computed using a SimBiology.SimFunction.Getting Started
Read and run driver.mlx for more details for how to use compute and plot ranking scores.
After installing this MATLAB Add-On, you can also type
>> help FeatureSelection
>> help FeatureSelection.histogram
in the MATLAB Command Window to get instructions for of how to use FeatureSelection.
This feature is supported in MATLAB® versions R2022a and later. Required products are SimBiology® and Statistics And Machine Learning Toolbox™.
[1] Ding C. and Peng H., Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computer Biology 2005; 3(2):185–205.
[2] Yang W., Wang K. and Zuo. W., Neighborhood component feature selection for high-dimensional data. Journal of Computers 2012; 7(1):161-168.

Cita come

Florian Augustin (2024). Feature Ranking for SimBiology (https://www.mathworks.com/matlabcentral/fileexchange/124755-feature-ranking-for-simbiology), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2022a
Compatibile con R2022a e release successive
Compatibilità della piattaforma
Windows macOS Linux
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Versione Pubblicato Note della release

Use 'results' instead of 'ranking' as out variable name in the example.


Fix: remove empty axes in histogram plots when reducing the number of plotted parameters via the NumParameters name-value argument.