Distance-based clustering of CGH data | Zendy

Jun Liu | Zendy; Jaaved Mohammed | Zendy; James F. Carter | Zendy; Sanjay Ranka | Zendy; Tamer Kahveci | Zendy; Michael Baudis | Zendy

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Distance-based clustering of CGH data

Author(s) -

Jun Liu,

Jaaved Mohammed,

James F. Carter,

Sanjay Ranka,

Tamer Kahveci,

Michael Baudis

Publication year - 2006

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/btl185

Subject(s) - cluster analysis , pairwise comparison , similarity (geometry) , cosine similarity , population , centroid , computer science , correlation , mathematics , data mining , artificial intelligence , medicine , geometry , environmental health , image (mathematics)

We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar.

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