Inferring quantitative models of regulatory networks from expression data | Zendy

Iftach Nachman | Zendy; Aviv Regev | Zendy; Nir Friedman | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Inferring quantitative models of regulatory networks from expression data

Author(s) -

Iftach Nachman,

Aviv Regev,

Nir Friedman

Publication year - 2004

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/bth941

Subject(s) - computer science , probabilistic logic , gene regulatory network , expression (computer science) , key (lock) , regulator , data mining , artificial intelligence , machine learning , computational biology , gene expression , biology , gene , genetics , programming language , computer security

Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems.

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