Network-based de-noising improves prediction from microarray data.

Autor: Kato T; Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp, Murata Y, Miura K, Asai K, Horton PB, Koji T, Fujibuchi W
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2006 Mar 20; Vol. 7 Suppl 1, pp. S4. Date of Electronic Publication: 2006 Mar 20.
DOI: 10.1186/1471-2105-7-S1-S4
Abstrakt: 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 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.
Conclusion: 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 drug responses from microarray data.
Databáze: MEDLINE
Externí odkaz: