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Found 5 papers, 2 papers with code
A Feature Selection Based on Perturbation Theory
Consider a supervised dataset $D=[A\mid \textbf{b}]$, where $\textbf{b}$ is the outcome column, rows of $D$ correspond to observations, and columns of $A$ are the features of the dataset.
High-Dimensional Feature Selection for Genomic Datasets
We cluster features first based on $\Delta\mathbf{x}$ and then using the entropy of features.
Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning
In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples.
Clustering, multicollinearity, and singular vectors
Let $A$ be a matrix with its pseudo-matrix $A^{\dagger}$ and set $S=I-A^{\dagger}A$.
Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease
However, GWAS studies have technical variations that affect the reproducibility of identified biomarkers, such as differences in genotyping platforms and selection criteria for individuals to be genotyped.
