Prediction errors in learning drug response from gene expression data - influence of labeling, sample size, and machine learning algorithm.

ABSTRACT: Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the

SUBMITTER: Bayer I 

PROVIDER: S-EPMC3720898 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature