Whole-genome expression analysis: challenges beyond clustering
Whole-genome expression analysis: challenges beyond clustering
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DOI：
10.1016/s0959-440x(00)00212-8
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发表时间：
2001-06-01
影响因子：
6.8
通讯作者：
Raychaudhuri, S
中科院分区：
文献类型：
--
作者：
Altman, RB;Raychaudhuri, S
测量生物系统中大多数或所有基因的表达提出了重大的分析挑战。最近的大量报告使用微阵列表达数据来研究不同的生物现象-从模式生物的基本过程到人类疾病的复杂方面。在最初的一系列基于相似性的数据聚类方法之后，该领域已经认识到了一些长期的挑战。首先，人们努力了解微阵列实验中的噪声和变化的来源，以增加生物信号。第二，努力将联合收割机表达数据与其他信息来源相结合，以提高可以得出的结论的范围和质量。最后，技术正在出现，以重建遗传相互作用的网络，以创建生物系统的综合和系统的模型。
Measuring the expression of most or all of the genes in a biological system raises major analytic challenges. A wealth of recent reports uses microarray expression data to examine diverse biological phenomena - from basic processes in model organisms to complex aspects of human disease. After an initial flurry of methods for clustering the data on the basis of similarity, the field has recognized some longer-term challenges. Firstly, there are efforts to understand the sources of noise and variation in microarray experiments in order to increase the biological signal. Secondly, there are efforts to combine expression data with other sources of information to improve the range and quality of conclusions that can be drawn. Finally, techniques are now emerging to reconstruct networks of genetic interactions in order to create integrated and systematic models of biological systems.