Name
Abstract | ||
|---|---|---|
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 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 drug responses from microarray data. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1186/1471-2105-7-S1-S4 | BMC Bioinformatics |
Keywords | Field | DocType |
microarray data,bioinformatics,heterogeneous network,microarrays,biological data,noise reduction,regression analysis,cross validation,algorithms,protein protein interaction,principal component analysis | Noise reduction,Data mining,Biological data,Correlation coefficient,Principal component regression,Computer science,Test data,Artificial noise,Bioinformatics,Heterogeneous network,Principal component analysis | Journal |
Volume | Issue | ISSN |
7 Suppl 1 | S-1 | 1471-2105 |
Citations | PageRank | References |
27 | 1.45 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsuyoshi Kato | 1 | 76 | 4.26 |
| Yukio Murata | 2 | 27 | 1.45 |
| Koh Miura | 3 | 27 | 1.45 |
| Kiyoshi Asai | 4 | 846 | 79.20 |
| Paul B Horton | 5 | 28 | 3.16 |
| Koji Tsuda | 6 | 1664 | 122.25 |
| Wataru Fujibuchi | 7 | 416 | 69.70 |