Single-layer artificial neural networks for gene expression analysis
Single-layer artificial neural networks for gene expression analysis
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DOI：
10.1016/j.neucom.2003.10.017
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发表时间：
2004-10-01
期刊：
影响因子：
6
通讯作者：
Tatineni, S
中科院分区：
文献类型：
--
作者：
Narayanan, A;Keedwell, EC;Tatineni, S
基因表达数据集的产生量不断增加，并在网络上提供。例如，通常使用 Affymetrix 基因芯片和斯坦福微阵列测量每个样本中数千个基因的 mRNA 表达水平。此类数据集通常分为不同的、客观测量的类别，通常是疾病状态或其他客观测量的表型。当前基因表达分析的一个主要问题是，鉴于测量的基因数量（通常为数千）和采样的个体数量（通常为数十）之间的差异，如何识别少数基因，这些基因单独或组合有助于对个体进行分类。以前的方法在面对问题的维度时倾向于使用无监督或监督技术，从而产生较小的基因簇，但簇本身并不能产生分类规则。时间微阵列数据尤其如此，它代表细胞、组织或生物体内基因随时间的活动。某些基因在特定时间点的表达水平可以通过其他基因在先前时间点的表达水平来控制。制药行业内的生物分子科学家和研究人员对数据中这些时间联系的提取非常感兴趣。如果能够找到这些所谓的基因网络来解释疾病的发生和进展，就可以设计针对特定基因的药物，从而使疾病不再进展，甚至从个体中根除。在本文中，我们描述了使用单层人工神经网络对非时间（分类）和时间微阵列数据进行建模的新颖实验。 (C) 2003 Elsevier B.V. 保留所有权利。
Gene expression datasets are being produced in increasing quantities and made available on the web. Several thousands of genes are usually measured for their mRNA expression levels per sample using Affymetrix gene chips and Stanford microarrays, for instance. Such datasets are normally separated into distinct, objectively measured classes, typically disease states or other objectively measured phenotypes. A major problem for current gene expression analysis is, given the disparity between the number of genes measured (typically, thousands) and number of individuals sampled (typically, dozens), how to identify the handful of genes which, individually or in combination, help classify individuals. Previous approaches when faced with the dimensionality of the problem have tended to use unsupervised or supervised techniques that result in smaller clusters of genes, but clusters by themselves do not yield classification rules. This is especially the case with temporal microarray data, which represents the activity of genes within a cell, tissue or organism over time. The expression levels of some genes at a particular time-point can be controlled by the expression levels of other genes at a previous time-point. It is the extraction of these temporal connections within the data that is of great interest to biomolecular scientists and researchers within the pharmaceutical industry. If these so-called gene networks can be found that explain disease inception and progression, drugs can be designed to target specific genes so that the disease either does not progress or is even eradicated from an individual. In this paper we describe novel experiments using single-layer artificial neural networks for modelling both non-temporal (classificatory) and temporal microarray data. (C) 2003 Elsevier B.V. All rights reserved.