title: Network-based de-noising improves prediction from microarray data.
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 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Kato
authors.forename: Yukio
authors.initials: Y
authors.lastname: Murata
authors.forename: Koh
authors.initials: K
authors.lastname: Miura
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Asai
authors.forename: Paul B
authors.initials: PB
authors.lastname: Horton
authors.forename: Tsuda
authors.initials: T
authors.lastname: Koji
authors.forename: Wataru
authors.initials: W
authors.lastname: Fujibuchi
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D025941:Protein Interaction Mapping; D012044:Regression Analysis; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein