Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/110568
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGomez Cabrero, David-
dc.contributor.authorMenche, Jörg-
dc.contributor.authorVargas, Claudia-
dc.contributor.authorCano Franco, Isaac-
dc.contributor.authorMaier, Dieter-
dc.contributor.authorBarabási, Albert László-
dc.contributor.authorTegnér, Jesper-
dc.contributor.authorRoca Torrent, Josep-
dc.contributor.authorSynergy‐COPD consortium-
dc.date.accessioned2017-05-08T10:14:31Z-
dc.date.available2017-05-08T10:14:31Z-
dc.date.issued2016-11-22-
dc.identifier.issn1471-2105-
dc.identifier.urihttp://hdl.handle.net/2445/110568-
dc.description.abstractBackground: Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers. Results: Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity. Conclusions: The developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers-
dc.format.extent13 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherBioMed Central-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1186/s12859-016-1291-3-
dc.relation.ispartofBMC Bioinformatics, 2016, vol. 17, num. Suppl 15, p. 441-
dc.relation.urihttps://doi.org/10.1186/s12859-016-1291-3-
dc.rightscc-by (c) Gomez-Cabrero, David et al., 2016-
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es-
dc.sourceArticles publicats en revistes (Medicina)-
dc.subject.classificationMalalties pulmonars obstructives cròniques-
dc.subject.classificationComorbiditat-
dc.subject.classificationMineria de dades-
dc.subject.classificationMarcadors bioquímics-
dc.subject.classificationMalalties de l'aparell digestiu-
dc.subject.classificationBioinformàtica-
dc.subject.otherChronic obstructive pulmonary diseases-
dc.subject.otherComorbidity-
dc.subject.otherData mining-
dc.subject.otherBiochemical markers-
dc.subject.otherDigestive system diseases-
dc.subject.otherBioinformatics-
dc.titleFrom comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.identifier.idgrec671208-
dc.date.updated2017-05-08T10:14:31Z-
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/270086/EU//SYNERGY-COPD-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.pmid28185567-
Appears in Collections:Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Articles publicats en revistes (Medicina)
Publicacions de projectes de recerca finançats per la UE

Files in This Item:
File Description SizeFormat 
671208.pdf2.99 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons