Fig 2. Unsupervised versus supervised learning. Unsupervised learning: multiple tumor samples are clustered into groups based on overall similarity of their gene expression profiles. This approach is useful for discovering previously unappreciated relationships. Supervised learning: multiple tumor samples from different known classes are used to train a model capable of classifying unknown samples. This model is then applied to a test set for class label assignment.

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