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Overall, GeDiRep provides a structured and interpretable framework for high-dimensional gene expression analysis by selecting reduced yet discriminative and biologically meaningful gene subsets.
The Grouping–Scoring–Modeling (G-S-M) framework is proposed, a knowledge-driven pipeline that anchors feature selection in established disease–gene associations that provides a reproducible computational framework for candidate biomarker discovery.
Portfolio construction aims to balance expected return and risk through effective asset allocation. This study proposes a portfolio formation framework that integrates machine learning-based return prediction with Markowitz mean–variance portfolio optimization. Random Forest, XGBoost, Multilayer Perceptron, and Support...
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