Data mining of gene expression changes in Alzheimer brain
Data mining of gene expression changes in Alzheimer brain
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DOI：
10.1016/j.artmed.2004.01.008
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发表时间：
2004-06-01
影响因子：
7.5
通讯作者：
Lach, B
中科院分区：
文献类型：
--
作者：
Walker, PR;Smith, B;Lach, B
全基因组转录谱分析是研究细胞状态的巨大复杂性的强大技术。此外，当应用于疾病组织时，它可以揭示基因表达的定量和定性变化，从而提供有关疾病背景或潜在基础的信息，并可以提供新的诊断方法。然而，从高密度微阵列获得的数据是非常复杂的，并提出了相当大的挑战，在数据挖掘。数据需要照顾的预处理和数据挖掘technology.This应用程序解决了处理来自两个已知的类（阿尔茨海默氏症和正常）的微阵列数据的问题。我们应用了三种不同的技术来发现与阿尔茨海默病（AD）相关的基因。在这项研究中确定的67个基因包括总共17个已知与阿尔茨海默氏症或其他神经系统疾病相关的基因。这比以前发表的任何阿尔茨海默氏症研究都要高。20个已知的基因，以前没有与疾病，已被确定，以及30个未表征的表达序列标签（EST）。鉴于已经成功鉴定出与AD相关的基因，通过这些研究，我们可以尝试确定治疗策略，以防止易感患者神经功能的特定成分丢失，或者能够刺激受损神经元中丢失的细胞功能的替代。我们的方法是在使用基因表达数据进行疾病建模（即分类和诊断）方面迈出了重要的一步。它还可以为基因功能鉴定、病理学、毒理基因组学和药物基因组学的未来做出贡献。(C)2004年由Elsevier B.V.出版
Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques.This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer `disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs.From these studies we can attempt to define therapeutic strategies that would prevent the toss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons.Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics. (C) 2004 Published by Elsevier B.V.