title: 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 network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium.
  
  
  CONCLUSIONS
  Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets.
authors.affiliation: Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. k.plaimas@dkfz.de
authors.forename: Kitiporn
authors.initials: K
authors.lastname: Plaimas
authors.forename: Jan-Phillip
authors.initials: JP
authors.lastname: Mallm
authors.forename: Marcus
authors.initials: M
authors.lastname: Oswald
authors.forename: Fabian
authors.initials: F
authors.lastname: Svara
authors.forename: Victor
authors.initials: V
authors.lastname: Sourjik
authors.forename: Roland
authors.initials: R
authors.lastname: Eils
authors.forename: Rainer
authors.initials: R
authors.lastname: König
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D017353:Gene Deletion; D023281:Genomics; D053858:Metabolic Networks and Pathways; D004364:Pharmaceutical Preparations / Q000378:metabolism; D012343:RNA, Transfer / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D004364:Pharmaceutical Preparations; D012343:RNA, Transfer