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

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Header Abstract Content Author and article information References Comments

Record: found
Abstract: found
Conference Proceedings: found

Is Open Access

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

Published

proceedings-article

Author(s): Lucía Prieto Santamaría ¹ ^, , Alejandro Rodríguez-González ¹

Publication date (Electronic preprint): 28 June 2022

Publisher: ScienceOpen

Conference name: RExPO22

Conference date: 2-3 September, 2022

Keywords: Drug repurposing, Drug repositioning, DISNET knowledge base, Human disease complex networks, Data-driven

Bookmark

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

Publisher: ScienceOpen

Publication date (Electronic preprint): 28 June 2022

Affiliations

[1 ] Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Spain

Author notes

[* ]Email: lucia.prieto.santamaria@ 123456upm.es .

Author information

Lucía Prieto Santamaría https://orcid.org/0000-0003-1545-3515

Alejandro Rodríguez-González https://orcid.org/0000-0001-8801-4762

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPPGCKMC.v1

SO-VID: 428618e3-9bf0-4bd8-92ee-3639431853b8

License:

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 .

Conference name: RExPO22

Conference location: Maastricht, Netherlands

Conference date: 2-3 September, 2022

History

Date received : 28 June 2022

Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Keywords: Drug repositioning,DISNET knowledge base,Human disease complex networks,Data-driven,Drug repurposing

References

Goh Kwang-Il, Cusick Michael E., Valle David, Childs Barton, Vidal Marc, Barabási Albert-László. The human disease network. Proceedings of the National Academy of Sciences. Vol. 104(21):8685–8690. 2007. Proceedings of the National Academy of Sciences. [Cross Ref]
García del Valle Eduardo P., Lagunes García Gerardo, Prieto Santamaría Lucía, Zanin Massimiliano, Menasalvas Ruiz Ernestina, Rodríguez-González Alejandro. Leveraging network analysis to evaluate biomedical named entity recognition tools. Scientific Reports. Vol. 11(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Lagunes-García Gerardo, Rodríguez-González Alejandro, Prieto-Santamaría Lucía, García del Valle Eduardo P., Zanin Massimiliano, Menasalvas-Ruiz Ernestina. DISNET: a framework for extracting phenotypic disease information from public sources. PeerJ. Vol. 8:2020. PeerJ. [Cross Ref]
Prieto Santamaría Lucía, Ugarte Carro Esther, Díaz Uzquiano Marina, Menasalvas Ruiz Ernestina, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. A data-driven methodology towards evaluating the potential of drug repurposing hypotheses. Computational and Structural Biotechnology Journal. Vol. 19:4559–4573. 2021. Elsevier BV. [Cross Ref]
Prieto Santamaría Lucía, Díaz Uzquiano Marina, Ugarte Carro Esther, Ortiz-Roldán Nieves, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. Integrating heterogeneous data to facilitate COVID-19 drug repurposing. Drug Discovery Today. Vol. 27(2):558–566. 2022. Elsevier BV. [Cross Ref]
Otero-Carrasco Belén, Prieto Santamaría Lucía, Ugarte Carro Esther, Caraça-Valente Hernández Juan Pedro, Rodríguez-González Alejandro. A Computational Drug Repositioning Method for Rare DiseasesBio-inspired Systems and Applications: from Robotics to Ambient Intelligence. p. 551–561. 2022. Springer International Publishing. [Cross Ref]
Prieto Santamaría Lucía, García del Valle Eduardo P., Zanin Massimiliano, Hernández Chan Gandhi Samuel, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. Classifying diseases by using biological features to identify potential nosological models. Scientific Reports. Vol. 11(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Prieto Santamaria Lucia, Garcia del Valle Eduardo P., Lagunes Garcia Gerardo, Zanin Massimiliano, Rodriguez Gonzalez Alejandro, Menasalvas Ruiz Ernestina, Perez Gallardo Yuliana, Hernandez Chan Gandhi Samuel. Analysis of New Nosological Models from Disease Similarities using Clustering. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020. IEEE. [Cross Ref]
García del Valle Eduardo P., Lagunes García Gerardo, Prieto Santamaría Lucía, Zanin Massimiliano, Menasalvas Ruiz Ernestina, Rodríguez-González Alejandro. DisMaNET: A network-based tool to cross map disease vocabularies. Computer Methods and Programs in Biomedicine. Vol. 207:2021. Elsevier BV. [Cross Ref]
Garcia del Valle Eduardo P., Lagunes Garcia Gerardo, Menasalvas Ruiz Ernestina, Prieto Santamaria Lucia, Zanin Massimiliano, Rodriguez-Gonzalez Alejandro. Completing Missing MeSH Code Mappings in UMLS Through Alternative Expert-Curated Sources. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). 2019. IEEE. [Cross Ref]

Comments

Comment on this article

[1] Goh Kwang-Il, Cusick Michael E., Valle David, Childs Barton, Vidal Marc, Barabási Albert-László. The human disease network. Proceedings of the National Academy of Sciences. Vol. 104(21):8685–8690. 2007. Proceedings of the National Academy of Sciences. [Cross Ref]

[2] García del Valle Eduardo P., Lagunes García Gerardo, Prieto Santamaría Lucía, Zanin Massimiliano, Menasalvas Ruiz Ernestina, Rodríguez-González Alejandro. Leveraging network analysis to evaluate biomedical named entity recognition tools. Scientific Reports. Vol. 11(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[3] Lagunes-García Gerardo, Rodríguez-González Alejandro, Prieto-Santamaría Lucía, García del Valle Eduardo P., Zanin Massimiliano, Menasalvas-Ruiz Ernestina. DISNET: a framework for extracting phenotypic disease information from public sources. PeerJ. Vol. 8:2020. PeerJ. [Cross Ref]

[4] Prieto Santamaría Lucía, Ugarte Carro Esther, Díaz Uzquiano Marina, Menasalvas Ruiz Ernestina, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. A data-driven methodology towards evaluating the potential of drug repurposing hypotheses. Computational and Structural Biotechnology Journal. Vol. 19:4559–4573. 2021. Elsevier BV. [Cross Ref]

[5] Prieto Santamaría Lucía, Díaz Uzquiano Marina, Ugarte Carro Esther, Ortiz-Roldán Nieves, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. Integrating heterogeneous data to facilitate COVID-19 drug repurposing. Drug Discovery Today. Vol. 27(2):558–566. 2022. Elsevier BV. [Cross Ref]

[6] Otero-Carrasco Belén, Prieto Santamaría Lucía, Ugarte Carro Esther, Caraça-Valente Hernández Juan Pedro, Rodríguez-González Alejandro. A Computational Drug Repositioning Method for Rare DiseasesBio-inspired Systems and Applications: from Robotics to Ambient Intelligence. p. 551–561. 2022. Springer International Publishing. [Cross Ref]

[7] Prieto Santamaría Lucía, García del Valle Eduardo P., Zanin Massimiliano, Hernández Chan Gandhi Samuel, Pérez Gallardo Yuliana, Rodríguez-González Alejandro. Classifying diseases by using biological features to identify potential nosological models. Scientific Reports. Vol. 11(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[8] Prieto Santamaria Lucia, Garcia del Valle Eduardo P., Lagunes Garcia Gerardo, Zanin Massimiliano, Rodriguez Gonzalez Alejandro, Menasalvas Ruiz Ernestina, Perez Gallardo Yuliana, Hernandez Chan Gandhi Samuel. Analysis of New Nosological Models from Disease Similarities using Clustering. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020. IEEE. [Cross Ref]

[9] García del Valle Eduardo P., Lagunes García Gerardo, Prieto Santamaría Lucía, Zanin Massimiliano, Menasalvas Ruiz Ernestina, Rodríguez-González Alejandro. DisMaNET: A network-based tool to cross map disease vocabularies. Computer Methods and Programs in Biomedicine. Vol. 207:2021. Elsevier BV. [Cross Ref]

[10] Garcia del Valle Eduardo P., Lagunes Garcia Gerardo, Menasalvas Ruiz Ernestina, Prieto Santamaria Lucia, Zanin Massimiliano, Rodriguez-Gonzalez Alejandro. Completing Missing MeSH Code Mappings in UMLS Through Alternative Expert-Curated Sources. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). 2019. IEEE. [Cross Ref]