An Jiyuan, Chen Yi-Ping Phoebe
Faculty of Science and Technology, School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, Victoria 3125, Australia.
J Biotechnol. 2008 Jun 30;135(3):233-40. doi: 10.1016/j.jbiotec.2008.04.004. Epub 2008 May 23.
A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs.
We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm.
The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/~phoebe/microarray.html.
一组基因及其基因表达水平用于对疾病组织和正常组织进行分类。由于微阵列中基因数量众多，在微阵列空间中存在大量用于划分不同基因类别的边界。边界基因（EGs）可以是共同调控的基因，它们也可以处于同一条通路中，或者受到相同的非编码基因（如小干扰RNA或微小RNA）的调控。EGs中的每个基因对于识别组织类别都至关重要。一个EG的基因表达变化可能导致组织从正常转变为疾病状态，反之亦然。找到EGs具有生物学重要性。在这项工作中，我们提出了一种算法来有效地找到这些EGs。
我们用五个微阵列数据集测试了我们的算法。将结果与用于寻找基因组并随后划分不同组织类别的基于边界的算法进行了比较。我们的算法找到的EGs数量比基于边界的算法显著更多。由于我们的算法在早期阶段就修剪了不相关的模式，其时间和空间复杂度比基于边界的算法要低得多。
所提出的算法在Linux平台上用C++实现。计算了五个微阵列数据集中的EGs。预处理后的数据集和发现的EGs可在http://www3.it.deakin.edu.au/~phoebe/microarray.html获取。
