Network-based de-noising improves prediction from microarray data

Background: Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. Results: We devised an extended version of the off-subspace noise-reduction (de-noising) method [13] to incorporate heterogeneous network data such as sequence similarity or proteinprotein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson's correlation coefficient bet...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tsuyoshi Kato, Yukio Murata, Koh Miura, Kiyoshi Asai, Paul Horton, Koji Tsuda, Wataru Fujibuchi
• Keywords: BMCBI 2006, Drug, Prediction, Prediction Performance