Mining coregulated biclusters from gene expression data

K. I. Lakshmi, C. P. Chandran
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Abstract

The objective of this paper is mining coregulated biclusters from gene expression data. Gene expression is the process which produces functional product from the gene information. Data mining is used to find relevant and useful information from databases. Clustering groups the genes according to the given conditions. Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix. In this paper a new algorithm, Enhanced Bimax algorithm is proposed based on the Bimax algorithm [7]. The normalization technique is included which is used to display a coregulated biclusters from gene expression data and grouping the genes in the particular order. In this work, Synthetic dataset is used to display the coregulated genes.
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从基因表达数据中挖掘共调控双聚类
本文的目的是从基因表达数据中挖掘共调控双聚类。基因表达是将基因信息转化为功能性产物的过程。数据挖掘用于从数据库中找到相关和有用的信息。聚类根据给定的条件对基因进行分组。双聚类算法属于一种不同的聚类算法,它同时对基因表达矩阵的行和列进行聚类。本文在极大值算法的基础上提出了一种新的算法——增强极大值算法[7]。规范化技术用于从基因表达数据中显示一个共调控的双聚类,并将基因按特定顺序分组。在这项工作中,使用合成数据集来显示共调控基因。
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