matFR

matFR implements 42 feature-ranking methods in MATLAB to identify informative and discriminative features from large biological and clinical datasets for applications such as precision medicine.

Key Features:

• Integration of Methods: Incorporates 42 diverse feature ranking methods, including 12 from FSLib, 9 mutual information-based methods, and 7 native MATLAB functions such as "rankfeatures", "relieff", and "lasso".
• Supervised vs. Unsupervised Methods: Provides 29 supervised and 13 unsupervised feature ranking methods.
• Theoretical Categorization: Implements method categories including 12 mutual information-based methods, 8 statistical analysis-based methods, and 8 structure learning-based methods.

Scientific Applications:

• Precision Medicine: Enables ranking of features to identify candidate biomarkers and characteristics relevant to diagnosis, prognosis, and treatment strategies.
• Data Interpretation: Facilitates comparison, investigation, and interpretation of selected features across datasets to aid understanding of complex biological and clinical data.
• Medical Imaging: Has been applied to sorting mammographic breast lesion features, demonstrating utility in medical imaging and diagnostics.

Methodology:

The integrated methods apply principles such as mutual information, statistical analysis, and structure clustering to estimate feature importance within specific measure spaces.