Li Xia, Rao Shaoqi, Zhang Tianwen, Guo Zheng, Zhang Qingpu, Moser Kathy L, Topol Eric J
Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China.
Sci China C Life Sci. 2004 Oct;47(5):396-405. doi: 10.1360/03yc0127.
The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simultaneously. Current analyses of microarray data focus on precise classification of biological types, for example, tumor versus normal tissues. A further scientific challenging task is to extract disease-relevant genes from the bewildering amounts of raw data, which is one of the most critical themes in the post-genomic era, but it is generally ignored due to lack of an efficient approach. In this paper, we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-driven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).
DNA微阵列技术的出现为通过同时监测数千个基因的活动来揭示生命奥秘带来了新的见解。目前对微阵列数据的分析主要集中在生物类型的精确分类上，例如肿瘤组织与正常组织的区分。一个更具科学挑战性的任务是从大量令人困惑的原始数据中提取与疾病相关的基因，这是后基因组时代最关键的主题之一，但由于缺乏有效的方法，这一任务通常被忽视。在本文中，我们提出了一种新颖的基因提取集成方法，该方法可以进行定制，以完成多种生物学任务，包括：（i）生物类型的精确分类；（ii）疾病基因挖掘；以及（iii）目标驱动的基因网络构建。我们还使用一个公共微阵列数据集对（i）和（ii）进行了数值应用，并将单独撰写一篇论文来阐述（iii）。
