Survival analysis of longitudinal microarrays | Zendy

Natasa Rajicic | Zendy; Dianne M. Finkelstein | Zendy; David Schoenfeld | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Survival analysis of longitudinal microarrays

Author(s) -

Natasa Rajicic,

Dianne M. Finkelstein,

David Schoenfeld

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/btl450

Subject(s) - dna microarray , significance analysis of microarrays , event (particle physics) , survival analysis , computational biology , gene expression profiling , time point , construct (python library) , gene expression , expression (computer science) , false discovery rate , longitudinal data , gene , biology , bioinformatics , data mining , computer science , genetics , medicine , philosophy , physics , quantum mechanics , programming language , aesthetics

The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays.

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