Knowledge-Driven Feature Selection with the Grouping–Scoring–Modeling Framework for Biomarker Discovery in High-Dimensional Transcriptomic Data
Biomarker discovery from high-dimensional transcriptomic data is frequently hindered by the “curse of dimensionality” and model selection bias. To address this, we propose the Grouping–Scoring–Modeling (G-S-M) framework, a knowledge-driven pipeline that anchors feature selection in established disease–gene associations...