An algorithm to assess the reliability of hierarchical clusters in gene expression data

被引：0

作者：

Avogadri, Roberto ^{[1
]}

Brioschi, Matteo ^{[2
]}

Ruffino, Francesca ^{[1
]}

Ferrazzi, Fulvia ^{[3
]}

Beghini, Alessandro ^{[2
]}

Valentini, Giorgio ^{[1
]}

机构：

[1] Univ Milan, DSI, I-20122 Milan, Italy

[2] Univ Milan, DBioGen Dip Biol & Genet Sci Med, I-20122 Milan, Italy

[3] Univ Pavia, Dipartimento Informat & Sistemist, Pavia, Italy

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS | 2008年 / 5179卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The validation of clusters discovered in bio-molecular data is a central issue in bioinformatics. Recently, stability-based methods have been successfully applied to the analysis of the reliability of clusterings characterized by a relatively low number of examples and clusters. Nevertheless, several problems in functional genomics are characterized by a very large number of examples and clusters. We present a stability-based algorithm to discover significant clusters in hierarchical clusterings with a large number of examples and clusters. Preliminary results on gene expression data of patients affected by Human Myeloid Leukemia, show how to apply the proposed method when thousands of gene clusters are involved.

引用

页码：764 / +

页数：3

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