Paper Info
Title
Graph Theory Enables Drug Repurposing - How A Mathematical Model Can Drive The Discovery Of Hidden Mechanisms Of Action
Abstract
We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.
Year
DOI
Venue
2013
10.1371/journal.pone.0084912
PLOS ONE
Keywords
Field
DocType
computer graphics,drug repositioning,vasoactive intestinal peptide,computational biology
Graph theory,Repurposing,Shortest path problem,Computer science,Computational linguistics,Stochastic process,Theoretical computer science,Exploit,Bioinformatics,Computer graphics,Graph (abstract data type)
Journal
Volume
Issue
ISSN
9
1
1932-6203
Citations 
PageRank 
References 
2
0.38
21
Authors
6
Name
Order
Citations
PageRank
Ruggero Gramatica120.72
Tiziana di Matteo2334.83
Stefano Giorgetti320.38
Massimo Barbiani420.38
Dorian Bevec520.38
Tomaso Aste65711.62