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      DISNET: Drug repositioning and disease understanding through complex networks creation and analysis

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      proceedings-article
        1 , , 1
      ScienceOpen
      RExPO22
      2-3 September, 2022
      Drug repurposing, Drug repositioning, DISNET knowledge base, Human disease complex networks, Data-driven
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            Abstract

            The DISNET project (https://disnet.ctb.upm.es) was conceived in the context of drug repurposing, aiming to build a large-scale disease network and integrating heterogeneous biomedical knowledge. It is being carried out in the Medical Data Analytics Laboratory (MEDAL) located at the Center for Biomedical Technology (CTB) of the Universidad Politécnica de Madrid (UPM). The principal objective of the DISNET project is to build a platform that enables researchers the creation of complex multilayer networks following the concepts of Human Disease Networks (HDNs), with the final purpose of generating new drug repurposing hypotheses. During recent years, DISNET has put together in an accessible knowledge base heterogeneous information that includes biomedical data obtained and integrated from both structured and unstructured sources. These data are organized in 3 topological levels: i) the phenotypic layer (with information regarding diseases and their associated symptoms); ii) the biologic layer (which stores molecular-shifted data related to diseases including genes, proteins, metabolic pathways, genetic variants, non-coding RNAs and so on); and iii) the pharmacologic layer (containing information of the drugs, their interactions and their connections to diseases). The main results derived from the execution of DISNET include a system able to automatically extract disease-symptom associations from different data sources by using Natural Language Processing (NLP). In the drug repurposing area, the DISNET project has suggested a data-driven methodology to evaluate new potentially repurposable drugs centred on disease-gene and disease-phenotype associations, and thus detecting significant differences between repurposing and non-repurposing data. In addition, a straightforward drug repurposing approach has been described for the particular case of COVID-19and other efforts have been made in the scope of rare diseases. Currently, new avenues are being explored to predict drug-disease links in the DISNET network by means of Graph Neural Networks (GNNs). Furthermore, the problems of classifying diseases in better nosological modelsand of mapping disease vocabularieshave also been tackled.

            Content

            Author and article information

            Conference
            ScienceOpen
            28 June 2022
            Affiliations
            [1 ] Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Spain
            Author notes
            Author information
            https://orcid.org/0000-0003-1545-3515
            https://orcid.org/0000-0001-8801-4762
            Article
            10.14293/S2199-1006.1.SOR-.PPPGCKMC.v1
            428618e3-9bf0-4bd8-92ee-3639431853b8

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO22
            Maastricht, Netherlands
            2-3 September, 2022
            History
            : 28 June 2022

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Drug repositioning,DISNET knowledge base,Human disease complex networks,Data-driven,Drug repurposing

            References

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