Network-based de-noising improves prediction from microarray data.
Network-based de-noising improves prediction from microarray data.
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DOI：
10.1186/1471-2105-7-s1-s4
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发表时间：
2006-03-20
影响因子：
3
通讯作者：
Fujibuchi W
中科院分区：
文献类型：
--
作者：
Kato T;Murata Y;Miura K;Asai K;Horton PB;Koji T;Fujibuchi W
从微阵列数据预测人类细胞对抗癌药物（化合物）的反应是一个具有挑战性的问题，这是由于微阵列的噪声特性以及活细胞对药物的反应的高方差。因此，有一个更实际的和强大的方法比标准方法的实际值预测的强烈需求。我们设计了一个扩展版本的非子空间降噪（去噪）方法，将异构网络数据，如序列相似性或蛋白质-蛋白质相互作用纳入一个单一的框架。使用该方法，我们首先对训练和测试数据的基因表达数据以及训练数据的药物反应数据进行去噪。然后，我们从去噪的输入数据预测每种药物的未知响应。为了确定去噪是否改善了预测，我们进行了12倍交叉验证来评估预测性能。我们使用真实响应值和预测响应值之间的Pearson相关系数作为预测性能。去噪提高了65%药物的预测性能。此外，我们发现，这种降噪方法是强大的和有效的，即使当大量的人工噪声被添加到输入数据。我们发现，我们的扩展的非子空间降噪方法结合异质生物数据是成功的，非常有用的，以提高预测人类细胞癌药物反应的微阵列数据。
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 dru responses from microarray data.