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Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection.

Brief Bioinform

November 2024

School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.

The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.

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