Combination methods in microarray analysis

Han-Yu Chuang, Hongfang Liu, Fang-An Chen, Cheng-Yan Kao, D. Hsu
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引用次数: 9

Abstract

Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurements in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combination is efficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.
微阵列分析中的组合方法
微阵列技术和实验可以在单个细胞mRNA样本中产生数千或数万个基因表达测量。从这些测量数据中选择信息丰富的差异基因列表一直是微阵列分析的中心问题。过去已经提出了许多鉴定信息基因的方法。然而,由于生物系统的复杂性,每种提出的方法似乎在特定的数据集或特定的实验中表现良好。为更广泛的实验范围和更广泛的数据集提出一种选择方法仍然是一个巨大的挑战。在本文中,我们采用了数据融合和等级分数图方法相结合的方法,这些方法在信息检索、模式识别与跟踪、分子相似性搜索等应用领域已经得到了成功的应用。我们的方法组合高效灵活,可以扩展成为微阵列基因表达分析的通用学习系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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