Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks
Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks
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DOI：
10.1263/jbb.96.421
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发表时间：
2003-11-01
影响因子：
2.8
通讯作者：
Shioya, S
中科院分区：
文献类型：
--
作者：
Huang, JH;Shimizu, H;Shioya, S
随着DNA微阵列技术的发展，基因表达谱等海量数据集正在不断积累。在本文中，我们专注于从海量数据集中挖掘生物学相关信息，如典型的表达模式和基因网络的相互联系。首先，采用自组织图谱（SOM）算法对基因表达数据进行聚类。然后，针对SOM提取的典型模式，采用三层人工神经网络（ANN）模型提取表达模式之间的关系；为了评价基于SOM的聚类分析，引入了生物学和统计学指标。为了验证所提出的利用人工神经网络提取表达模式之间关系的方案的有效性，创建了一个测试数据集并用于测试。最后，我们提取并可视化了酵母细胞周期中G1早期、G1晚期、S、G2和M期典型模式的相互联系。
Massive datasets such as gene expression profiles are accumulating along with the development of DNA microarray technologies. In this paper, we focus on mining biological relevant information such as typical expression patterns and the interconnections of gene networks from massive datasets. At first, the algorithm of a self-organizing map (SOM) was used to cluster gene expression data. Then, for the typical patterns extracted by the SOM, a three-layer artificial neural network (ANN) model was used to extract the relationships between the expression patterns. In order to evaluate the clustering analysis based on the SOM, biological and statistical indices were introduced. To validate the efficiency of the scheme proposed for extracting the relationships between the expression patterns with the ANN, a test dataset was created and used for the test. Finally, the interconnections of a typical pattern of early G1, late G1, S, G2, and M phases in a yeast cell cycle were extracted and visualized.