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This article is part of the supplement: NIPS workshop on New Problems and Methods in Computational Biology

Open Access Proceedings

Network-based de-noising improves prediction from microarray data

Tsuyoshi Kato12, Yukio Murata3, Koh Miura3, Kiyoshi Asai12, Paul B Horton2, Koji Tsuda2 and Wataru Fujibuchi2*

Author Affiliations

1 Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 – 8562, Japan

2 AIST Computational Biology Research Center, 2-42, Aomi, Koto-ku, Tokyo, 135-0064, Japan

3 Division of Biological Regulation and Oncology, Department of Surgery, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan

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BMC Bioinformatics 2006, 7(Suppl 1):S4  doi:10.1186/1471-2105-7-S1-S4

Published: 20 March 2006



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.


We devised an extended version of the off-subspace noise-reduction (de-noising) method [1] to incorporate heterogeneous network data such as sequence similarity or protein-protein 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 between the true and predicted response values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data.


We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer dru responses from microarray data.