Exploiting sample variability to enhance multivariate analysis of microarray data | Zendy

Carla S. MöllerLevet | Zendy; Catharine West | Zendy; Crispin Miller | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Exploiting sample variability to enhance multivariate analysis of microarray data

Author(s) -

Carla S. MöllerLevet,

Catharine West,

Crispin Miller

Publication year - 2007

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/btm441

Subject(s) - data mining , computer science , pairwise comparison , sample (material) , microarray analysis techniques , gene chip analysis , multivariate statistics , correlation , exploit , dna microarray , machine learning , artificial intelligence , gene expression , mathematics , gene , biology , genetics , chemistry , geometry , computer security , chromatography

Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses.

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