Model-free unsupervised gene set screening based on information enrichment in expression profiles | Zendy

Atushi Niida | Zendy; Seiya Imoto | Zendy; Rui Yamaguchi | Zendy; Masao Nagasaki | Zendy; André Fujita | Zendy; Teppei Shimamura | Zendy; Satoru Miyano | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Model-free unsupervised gene set screening based on information enrichment in expression profiles

Author(s) -

Atushi Niida,

Seiya Imoto,

Rui Yamaguchi,

Masao Nagasaki,

André Fujita,

Teppei Shimamura,

Satoru Miyano

Publication year - 2010

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/btq592

Subject(s) - benchmark (surveying) , set (abstract data type) , computer science , data set , data mining , computational biology , expression (computer science) , gene expression , artificial intelligence , gene , pattern recognition (psychology) , biology , genetics , geodesy , programming language , geography

A number of unsupervised gene set screening methods have recently been developed for search of putative functional gene sets based on their expression profiles. Most of the methods statistically evaluate whether the expression profiles of each gene set are fit to assumed models: e.g. co-expression across all samples or a subgroup of samples. However, it is possible that they fail to capture informative gene sets whose expression profiles are not fit to the assumed models.

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