Ahnert S E, Willbrand K, Brown F C S, Fink T M A
Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK.
Bioinformatics. 2006 Jun 15;22(12):1471-6. doi: 10.1093/bioinformatics/btl121. Epub 2006 Apr 3.
Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many cases the aim is to identify pattern in the data series generated by successive microarray measurements.
Here we introduce a new method of detecting pattern in microarray data series which is independent of the nature of this pattern. Our approach provides a measure of the algorithmic compressibility of each data series. A series which is significantly compressible is much more likely to result from simple underlying mechanisms than series which are incompressible. Accordingly, the gene associated with a compressible series is more likely to be biologically significant. We test our method on microarray time series of yeast cell cycle and show that it blindly selects genes exhibiting the expected cyclic behaviour as well as detecting other forms of pattern. Our results successfully predict two independent non-microarray experimental studies.
近年来随着微阵列技术的出现，生物学家面临的挑战是从每个微阵列实验中测量的数千个基因表达水平中识别出感兴趣的基因。在许多情况下，目的是识别由连续微阵列测量产生的数据系列中的模式。
在此我们介绍一种检测微阵列数据系列中模式的新方法，该方法与这种模式的性质无关。我们的方法提供了每个数据系列算法可压缩性的一种度量。一个可显著压缩的系列比不可压缩的系列更有可能源于简单的潜在机制。因此，与可压缩系列相关的基因更有可能具有生物学意义。我们在酵母细胞周期的微阵列时间序列上测试了我们的方法，结果表明它能盲目地选择呈现预期周期行为的基因以及检测其他形式的模式。我们的结果成功地预测了两项独立的非微阵列实验研究。
