Yang Andy C, Hsu Hui-Huang, Lu Ming-Da, Tseng Vincent S, Shih Timothy K
Int J Data Min Bioinform. 2014;10(2):121-45. doi: 10.1504/ijdmb.2014.064010.
Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.
选择信息丰富的基因是微阵列基因表达数据分析中最重要的任务。在这项工作中，我们旨在从微阵列基因表达数据中识别调控基因对。然而，微阵列数据通常包含多个缺失的表达值。因此，在对调控基因对进行进一步处理之前，需要进行缺失值插补。我们开发了一种新颖的方法，首先通过结合k近邻（KNN）、动态时间规整（DTW）和基因本体（GO）来插补微阵列时间序列数据中的缺失值。在插补缺失值之后，我们基于我们提出的基因对DTW-GO距离测量来进行基因调控预测。实验结果表明，与真实微阵列数据集上现有的缺失值插补方法相比，我们的方法更准确。此外，与其他方法相比，我们的方法还能发现更多文献中已知的调控基因对。
