title: Biological network inference for drug discovery.
abstract: A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.
authors.affiliation: The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Piazza Manifattura 1 - 38068 Rovereto, Italy. paola.lecca@unitn.it
authors.forename: Paola
authors.initials: P
authors.lastname: Lecca
authors.forename: Corrado
authors.initials: C
authors.lastname: Priami
mesh_terms: D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways