基于支持向量机集成改进的多目标聚类基因表达数据分析。

Q2 Medicine
Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay
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引用次数: 6

摘要

微阵列技术有助于同时监测不同实验条件下数千个基因的表达水平。聚类是一种流行的数据挖掘工具,可以应用于微阵列基因表达数据来识别共表达基因。传统的聚类方法大多对单一的聚类优度准则进行优化,因此可能无法在所有类型的数据集上表现良好。基于此,本文通过一种新的基于支持向量机分类的聚类集成方法,改进了一种同时优化聚类紧密度和分离度的多目标聚类技术。通过将MOCSVMEN (multi - objective Clustering with Support Vector Machine based ENsemble)算法的性能与现有几种知名的微阵列数据聚类算法进行比较,证明了MOCSVMEN算法的优越性。两个现实生活中的基准基因表达数据集已被用于测试不同算法的比较性能。最近开发的一种度量,称为生物同质性指数(BHI),它计算关于功能注释的聚类优度,已用于比较目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene expression data analysis using multiobjective clustering improved with SVM based ensemble.

Microarray technology facilitates the monitoring of the expression levels of thousands of genes over different experimental conditions simultaneously. Clustering is a popular data mining tool which can be applied to microarray gene expression data to identify co-expressed genes. Most of the traditional clustering methods optimize a single clustering goodness criterion and thus may not be capable of performing well on all kinds of datasets. Motivated by this, in this article, a multiobjective clustering technique that optimizes cluster compactness and separation simultaneously, has been improved through a novel support vector machine classification based cluster ensemble method. The superiority of MOCSVMEN (MultiObjective Clustering with Support Vector Machine based ENsemble) has been established by comparing its performance with that of several well known existing microarray data clustering algorithms. Two real-life benchmark gene expression datasets have been used for testing the comparative performances of different algorithms. A recently developed metric, called Biological Homogeneity Index (BHI), which computes the clustering goodness with respect to functional annotation, has been used for the comparison purpose.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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