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首页> 外文会议>International Conference on Artificial Neural Networks(ICANN 2006) pt.2; 20060910-14; Athens(GR) >Missing Value Estimation for DNA Microarrays with Mutliresolution Schemes
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Missing Value Estimation for DNA Microarrays with Mutliresolution Schemes

机译：具有多分辨率方案的DNA芯片缺失值估计

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摘要

The expression pattern of a gene across time can be considered as a signal; a microarray experiment is collection of thousands of such signals where due to instrument failure, human errors and technology limitations, values at some time instances are usually missing. Furthermore, in some microarray experiments the gene signals are not sampled at regular time intervals, which renders the direct use of well established frequency-temporal signal analysis approaches such as the wavelet transform problematic. In this work we evaluate a novel mul-tiresolution method, known as the lifting transform to estimate missing values in time series microarray data. Though the lifting transform has been developed to deal with irregularly spaced data its usefulness for the estimation of missing values in microarray data has not been examined in detail yet. In this framework we evaluate the lifting transform against the wavelet transform, a moving average method and a zero imputation on 5 data sets from the cell cycle and the sporula-tion of the saccharomyces cerevisiae.
机译：跨时间的基因表达模式可以看作是信号。微阵列实验是成千上万个此类信号的集合，由于仪器故障，人为错误和技术局限性，有时有时会缺少值。此外，在一些微阵列实验中，基因信号不是按固定的时间间隔采样的，这使得直接使用成熟的频率-时间信号分析方法（例如小波变换）成为问题。在这项工作中，我们评估一种新颖的多分辨率方法，称为提升变换，以估计时间序列微阵列数据中的缺失值。尽管已经开发了提升变换来处理不规则间隔的数据，但是尚未详细检查其在微阵列数据中估计缺失值的有用性。在此框架中，我们针对细胞周期和酿酒酵母的孢子形成对5个数据集进行了针对小波变换的提升变换，移动平均方法和归零估计。

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