calculate_mean_cv_scores

Calculate Mean Cross-Validation Scores for Various Feature Selection Methods

The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (<https://scikit-learn.org/stable/index.html>) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) <doi:10.1002/pro.4218>, using 'clusterProfiler' (Wu et al., 2021) <doi:10.1016/j.xinn.2021.100141> and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) <doi:10.1016/j.gpb.2022.04.008>. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.

Damir Zhakparov

GeneSelectR

'GeneSelectR' - Comprehensive Feature Selection Workflow for
Bulk RNAseq Datasets

calculate_mean_cv_scores function

<dl><dt>selected_pipelines</dt>
<dd>A list of pipelines for different feature selection methods.</dd>
<dt>cv_best_score</dt>
<dd>A list or vector of cross-validation scores.</dd></dl>

Arguments

Calculate Mean Cross-Validation Scores for Various Feature Selection Methods — calculate_mean_cv_scores

<dl>

<dt>selected_pipelines</dt>
<dd>A list of pipelines for different feature selection methods.</dd>


<dt>cv_best_score</dt>
<dd>A list or vector of cross-validation scores.</dd>

</dl>

calculate_mean_cv_scores: Calculate Mean Cross-Validation Scores for Various Feature Selection Methods

Description

Usage

Value

Arguments