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Found 2 papers, 1 papers with code
Relief-Based Feature Selection: Introduction and Review
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations.
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e. g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e. g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e. g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable.
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