Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically | Zendy

David R. Bickel | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically

Author(s) -

David R. Bickel

Publication year - 2003

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/btg092

Subject(s) - outlier , data mining , computer science , cluster analysis , measure (data warehouse) , euclidean distance , rank (graph theory) , cluster (spacecraft) , set (abstract data type) , expression (computer science) , software , pattern recognition (psychology) , mathematics , artificial intelligence , combinatorics , programming language

The success of each method of cluster analysis depends on how well its underlying model describes the patterns of expression. Outlier-resistant and distribution-insensitive clustering of genes are robust against violations of model assumptions.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research