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Table 1 Genes/features selected by disparate feature selection techniques

From: A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data

Feature selection techniquesa Features/Genes (No.)
Filtered by low expression 8281
GSVA feature enrichment 1161
sRAP 837
SVM-RFE 38
RF-based Positive MDA 3339
T-test 60
  1. aFeature selection is based on five different methodologies based on machine learning algorithms (SVM and RF) and also that of traditional differentially expressed genes (sRAP), t-test based analysis (limma) and genes in deregulated pathways (GSVA)