Published 2025-05-01
“…Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. Ensemble ML
algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur
models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. …”
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