RT Journal Article
SR Electronic
T1 DISNET: Disease understanding through complex networks creation and analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 428201
DO 10.1101/428201
A1 Gerardo Lagunes García
A1 Alejandro Rodríguez González
A1 Lucía Prieto Santamaría
A1 Eduardo P. García del Valle
A1 Massimiliano Zanin
A1 Ernestina Menasalvas Ruiz
YR 2018
UL http://biorxiv.org/content/early/2018/09/29/428201.abstract
AB Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper we present DISNET (Drug repositioning and disease understanding through complex networks creation and analysis), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical data bases, and to enable the creation of customisable disease networks. We here present the main functionalities of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and MayoClinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.Database URL: http://disnet.ctb.upm.es