Clustering of time-course gene expression data using a mixed-effects model with B-splines | Zendy

Yihui Luan | Zendy; Hongzhe Li | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Clustering of time-course gene expression data using a mixed-effects model with B-splines

Author(s) -

Yihui Luan,

Hongzhe Li

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/btg014

Subject(s) - cluster analysis , gene expression , gene , computational biology , gene cluster , computer science , gene regulatory network , microarray analysis techniques , expression (computer science) , gene expression profiling , data mining , biology , genetics , artificial intelligence , programming language

Time-course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. To account for time dependency of the gene expression measurements over time and the noisy nature of the microarray data, the mixed-effects model using B-splines was introduced. This paper further explores such mixed-effects model in analyzing the time-course gene expression data and in performing clustering of genes in a mixture model framework.

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