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Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships
dc.contributor.author | Sánchez, Jon |
dc.contributor.author | Tejero, Héctor |
dc.contributor.author | Fernández, José María |
dc.contributor.author | Juan, David |
dc.contributor.author | Urda García, Beatriz |
dc.contributor.author | Capella Gutiérrez, Salvador |
dc.contributor.author | Al-Shahrour, Fátima |
dc.contributor.author | Tabarés Seisdedos, Rafael |
dc.contributor.author | Baudot, Anais |
dc.contributor.author | Pancaldi, Vera |
dc.contributor.author | Valencia, Alfonso |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2020-06-12T13:49:11Z |
dc.date.available | 2020-06-12T13:49:11Z |
dc.date.issued | 2020 |
dc.identifier.citation | Sánchez, J. [et al.]. Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships. "Nature Communications", 2020, vol. 11, núm. 2854. |
dc.identifier.issn | 2041-1723 |
dc.identifier.uri | http://hdl.handle.net/2117/190646 |
dc.description.abstract | Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence of a specific disease in patients affected by other conditions. We present here a disease interaction network inferred from similarities between patients’ molecular profiles, which significantly recapitulates epidemiologically documented comorbidities. Furthermore, we identify disease patient-subgroups that present different molecular similarities with other diseases, some of them opposing the general tendencies observed at the disease level. Analyzing the generated patient-subgroup network, we identify genes involved in such relations, together with drugs whose effects are potentially associated with the observed comorbidities. All the obtained associations are available at the disease PERCEPTION portal (http://disease-perception.bsc.es). |
dc.description.sponsorship | This work was supported by a Ph.D. Fellowship (BES-2016-077403) and funded by the Spanish Ministry of Economics and Competitiveness (BFU2015-71241-R, RTI2018-096653-B-I00). The Coordination Node led by A.V. at the Barcelona Supercomputing Center (BSC) is a member of the Spanish National Bioinformatics Institute (INB), ISCIII-Bioinformatics platform and is supported by grant PT17/0009/0001 of the Acción Estratégica en Salud 2013-2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). V.P. is supported by INSERM, the Fondation Toulouse Cancer Santé and Pierre Fabre Research Institute as part of the Chair of Bioinformatics in Oncology of the CRCT. A.B. has received funding from the Excellence Initiative of Aix-Marseille University—A*Midex, a French “Investissements d’Avenir” program. D.J. was supported by Juan de la Cierva fellowship (FJCI-2016-29558) from the Ministerio de Ciencia, Innovación y Universidades. H.T. and F.A.-S. were supported by the Instituto de Salud Carlos III (ISCIII), Spanish National Bioinformatics Institute (INB) Grant (PT17/0009/0011—ISCIII-SGEFI/ERDF) and Marie-Curie Career Integration Grant (CIG334361). J.M.F. and S.C.-G. were supported by the Instituto de Salud Carlos III (ISCIII), Spanish National Bioinformatics Institute (INB) Grant (PT17/0009/0001—ISCIII-SGEFI/ERDF). R.T.-S. was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. |
dc.format.extent | 13 p. |
dc.language.iso | eng |
dc.publisher | Springer Nature |
dc.rights | Attribution 3.0 Spain |
dc.rights | Attribution 4.0 International (CC BY 4.0) |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject.lcsh | Biomedical engineering |
dc.subject.lcsh | Diseases |
dc.subject.lcsh | Molecular genetics |
dc.subject.lcsh | Comorbidity |
dc.subject.lcsh | Medical care. |
dc.subject.other | Comorbidity |
dc.subject.other | Molecular similarity |
dc.subject.other | Biomedical research |
dc.subject.other | Disease PERCEPTION portal |
dc.subject.other | Gene expression profiles |
dc.subject.other | Protein−protein interaction |
dc.subject.other | Phenotypes |
dc.subject.other | Healthcare |
dc.title | Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships |
dc.type | Article |
dc.subject.lemac | Enginyeria biomèdica |
dc.subject.lemac | Malalties |
dc.subject.lemac | Genètica molecular |
dc.identifier.doi | 10.1038/s41467-020-16540-x |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.nature.com/articles/s41467-020-16540-x |
dc.rights.access | Open Access |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/BES-2016-077403 |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096653-B-I00/ES/EXTRACCION DE PERFILES DE COMORBILIDAD PERSONALIZADOS Y DE TRAYECTORIAS BASADAS EN DATOS MULTI-OMICOS/ |
local.citation.publicationName | Nature Communications |
local.citation.volume | 11 |
local.citation.number | 2854 |
dc.relation.dataset | http://disease-perception.bsc.es |
dc.relation.dataset | https://www.ebi.ac.uk/arrayexpress/) and GEO (http://www.ncbi.nlm.nih.gov/geo |
dc.relation.dataset | https://github.com/jonsv89/Disease_PERCEPTION |
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