Unsupervised methods are a class of machine learning techniques used to identify patterns and structures in data without predefined labels or categories. These methods play a crucial role in data exploration, clustering, and dimensionality reduction, making them especially useful in analyzing complex biological datasets from metabolomics and genomics integration, where relationships between variables are not immediately apparent.
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