Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites | Zendy

Fangping Mu | Zendy; Pat J. Unkefer | Zendy; Clifford J. Ünkefer | Zendy; William S. Hlavacek | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites

Author(s) -

Fangping Mu,

Pat J. Unkefer,

Clifford J. Ünkefer,

William S. Hlavacek

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/btl535

Subject(s) - false positive paradox , kegg , support vector machine , chemistry , computer science , classifier (uml) , training set , catalysis , combinatorial chemistry , computational biology , biological system , artificial intelligence , biochemistry , biology , gene , gene expression , transcriptome

Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions.

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