Biology subjects

Menasalvas Ruiz, E.

Publications and source records attributed to Menasalvas Ruiz, E..

2 recordsLinked to original sources

bioRxiv2018

DISNET: Disease understanding through complex networks creation and analysis

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.\n\nDatabase URL: http://disnet.ctb.upm.es

bioinformatics↗

bioRxiv2018

Disease networks and their contribution to disease understanding and drug repurposing. A survey of the state of the art

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

bioinformatics↗