Scientists using machine learning tools to analyze biomedical data often turn to neural network algorithms, but before these models became popular, another simpler type of machine learning algorithm called kernel methods were commonly used. Kernel methods work by first applying straightforward operations to transform data and then training a simple model on the transformed data.
. These datasets describe the effects of drugs on cancer cell lines across millions of drug and cell line combinations.
The scientists then applied their projection and translation techniques to their model so that it could complete the second task: to predict the effect of the drug on new cancer cell lines that have much less data. The projection transformation corrects the model's predictions on the second task by recognizing when the prediction errors are falling into categories that can be easily corrected to the right category.
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