Machine learning based analyses on metabolic networks supports high-throughput knockout screens.

ABSTRACT:

Background

Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics.

Results

This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the net

SUBMITTER: Plaimas K 

PROVIDER: S-EPMC2526078 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature