https://targetproteinligansign....al.com/index.php/ext
Machine-learning-based practices may work very well for identifying important predictors to generate parsimonious results, but such 'black box' variable selection restricts interpretability, and variable significance examined from an individual design is biased. We propose a robust and interpretable adjustable choice strategy utilizing the recently created Shapley variable significance cloud (ShapleyVIC) that accounts for variability in variable relevance
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