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首页> 外文会议>Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA >Gene Expression Signature Discovery using Independent Component Analysis
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Gene Expression Signature Discovery using Independent Component Analysis

机译：使用独立成分分析的基因表达签名发现

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摘要

With the advent of high throughput DNA microarrays and the large cross section of the gene activity, or expression, that it provides, the potential for the early detection and diagnosis of cancer before morphogenesis has dramatically increased. While many statistical methods, such as cluster analysis, have been developed to tap into this enormous information source, a reliable method of early detection and diagnosis has yet to be developed. In this paper we propose using independent component analysis (ICA) as a first step in a process to identify diseased tissue solely based on its gene expression profile. In the ICA vernacular, a set of genes can be viewed as the sensors while certain biological processes, including the manifestation of a given disease, can be viewed as the signals. The goal then is to identify one or more 'demixed' signals, or signatures, that can be associated with the given disease. The demixing matrix can then be used to find the biological signals of an unknown sample, which might, in turn, be used for diagnosis when compared to the previously determined disease signatures. In this paper we explore the use of this technique on a previously studied melanoma dataset (Bittner, et. al., 2000).
机译：随着高通量DNA微阵列的出现以及它提供的基因活性或表达的大截面，在形态发生显着增加之前进行早期检测和诊断癌症的潜力。尽管开发了许多统计方法（例如聚类分析）来利用这一巨大的信息源，但尚未开发出可靠的早期检测和诊断方法。在本文中，我们建议使用独立成分分析（ICA）作为仅根据病变组织的基因表达谱来鉴定病变组织的第一步。在ICA方面，可以将一组基因视为传感器，而将某些生物学过程（包括给定疾病的表现）视为信号。然后，目标是识别可以与给定疾病相关的一个或多个“消除”信号或特征。然后，可以使用混合矩阵找到未知样品的生物学信号，与先前确定的疾病特征进行比较时，可以将其用于诊断。在本文中，我们探索了在以前研究过的黑色素瘤数据集上使用此技术的方法（Bittner等，2000）。

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