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Machine learning is reshaping biomarker discovery by extracting meaningful signals from complex, high-dimensional biological data. Supervised models pinpoint biomarkers linked to known clinical outcomes, while unsupervised approaches uncover hidden patterns and novel biomarker candidates that may not be apparent a priori. Python ties this workflow together with a cohesive ecosystem for data preprocessing, modeling, validation, and visualization—making biomarker discovery faster, more reproducibl
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