eprintid: 1396304
rev_number: 33
eprint_status: archive
userid: 608
dir: disk0/01/39/63/04
datestamp: 2013-06-13 19:06:22
lastmod: 2021-10-05 00:31:34
status_changed: 2013-06-13 19:06:22
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Gramatica, R
creators_name: Di Matteo, T
creators_name: Giorgetti, S
creators_name: Barbiani, M
creators_name: Bevec, D
creators_name: Aste, T
title: Graph theory enables drug repurposing - how a mathematical model can drive the discovery of hidden mechanisms of action.
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: ©� 2014 Gramatica et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PMCID: PMC3886994
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.
date: 2014-01-09
official_url: http://dx.doi.org/10.1371/journal.pone.0084912
vfaculties: VENG
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_source: PubMed
elements_id: 876845
doi: 10.1371/journal.pone.0084912
pii: PONE-D-13-25422
lyricists_name: Aste, Tomaso
lyricists_id: TASTE72
full_text_status: public
publication: PLoS One
volume: 9
number: 1
article_number: e84912
event_location: United States
citation:        Gramatica, R;    Di Matteo, T;    Giorgetti, S;    Barbiani, M;    Bevec, D;    Aste, T;      (2014)    Graph theory enables drug repurposing - how a mathematical model can drive the discovery of hidden mechanisms of action.                   PLoS One , 9  (1)    , Article e84912.  10.1371/journal.pone.0084912 <https://doi.org/10.1371/journal.pone.0084912>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1396304/1/journal.pone.0084912.pdf