Clustering of gene expression data using a local shape-based similarity measure | Zendy

Rajarajeswari Balasubramaniyan | Zendy; Eyke Hüllermeier | Zendy; Nils Weskamp | Zendy; Jörg Kämper | Zendy

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Clustering of gene expression data using a local shape-based similarity measure

Author(s) -

Rajarajeswari Balasubramaniyan,

Eyke Hüllermeier,

Nils Weskamp,

Jörg Kämper

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti095

Subject(s) - cluster analysis , measure (data warehouse) , similarity (geometry) , similarity measure , computer science , data mining , expression (computer science) , pattern recognition (psychology) , gene expression , computational biology , artificial intelligence , gene , biology , genetics , image (mathematics) , programming language

Microarray technology enables the study of gene expression in large scale. The application of methods for data analysis then allows for grouping genes that show a similar expression profile and that are thus likely to be co-regulated. A relationship among genes at the biological level often presents itself by locally similar and potentially time-shifted patterns in their expression profiles.

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