Network-based de-noising improves prediction from microarray data

Tsuyoshi Kato, Yukio Murata, Koh Miura, Kiyoshi Asai, Paul B. Horton, Koji Tsuda, Wataru Fujibuchi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 [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 denoising 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 dru responses from microarray data.

Original languageEnglish
Article numberS4
JournalBMC Bioinformatics
Volume7
Issue numberSUPPL.1
DOIs
Publication statusPublished - 2006 Mar 20

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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