AN ADAPTIVE SEGMENTATION METHOD BASED ON GAUSSIAN MIXTURE MODEL (GMM) CLUSTERING FOR DNA MICROARRAY

被引：4

作者：

Parthasarathy, M. ^{[1
]}

Ramya, R. ^{[1
]}

Vijaya, A. ^{[2
]}

机构：

[1] Govt Arts Coll, Dept Comp Sci, Salem 636007, Tamil Nadu, India

[2] Govt Arts Coll, Dept Comp Applicat, Salem 636007, Tamil Nadu, India

来源：

2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS (ICICA 2014) | 2014年

关键词：

DNA gene expressions; microarray gridding; Gaussian mixture model; Expectation maximization Mathematical morphology; histogram analysis;

D O I：

10.1109/ICICA.2014.24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Microarray allows us to efficiently analyse valuable gene expression data. In this paper we propose a effective methodology for analysis of microarrays. Earlier a new gridding algorithm is proposed to address all individual spots and to determine their borders. Then, a classical Gaussian Mixture Model (GMM) is used to analyse array spots more flexibly and adaptively. The Expectation Maximization (EM) algorithm is used to estimate GMM parameters by Maximum Likelihood (ML) approach. In this paper, we also addressing the problem of artifacts by detecting and compensate using GMM mixture components and artifacts data present in foreground and background spots are corrected by performing mathematical morphology and histogram analysis methods

引用

页码：73 / 77

页数：5

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