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dc.contributor.author | Gomez Cabrero, David |
---|---|
dc.contributor.author | Menche, Jörg |
dc.contributor.author | Vargas, Claudia |
dc.contributor.author | Cano Franco, Isaac |
dc.contributor.author | Maier, Dieter |
dc.contributor.author | Barabási, Albert László |
dc.contributor.author | Tegnér, Jesper |
dc.contributor.author | Roca Torrent, Josep |
dc.contributor.author | Synergy‐COPD consortium |
dc.date | 2017-05-08T10:14:31Z |
dc.date | 2017-05-08T10:14:31Z |
dc.date | 2016-11-22 |
dc.date | 2017-05-08T10:14:31Z |
dc.identifier | 1471-2105 |
dc.identifier | 671208 |
dc.identifier | 28185567 |
dc.identifier.uri | http://hdl.handle.net/2445/110568 |
dc.description | Background: 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 | 13 p. |
dc.format | application/pdf |
dc.language | eng |
dc.publisher | BioMed Central |
dc.relation | Reproducció del document publicat a: https://doi.org/10.1186/s12859-016-1291-3 |
dc.relation | BMC Bioinformatics, 2016, vol. 17, num. Suppl 15, p. 441 |
dc.relation | https://doi.org/10.1186/s12859-016-1291-3 |
dc.rights | cc-by (c) Gomez-Cabrero, David et al., 2016 |
dc.rights | http://creativecommons.org/licenses/by/3.0/es |
dc.rights | info:eu-repo/semantics/openAccess |
dc.subject | Malalties pulmonars obstructives cròniques |
dc.subject | Comorbiditat |
dc.subject | Mineria de dades |
dc.subject | Marcadors bioquímics |
dc.subject | Malalties de l'aparell digestiu |
dc.subject | Bioinformàtica |
dc.subject | Chronic obstructive pulmonary diseases |
dc.subject | Comorbidity |
dc.subject | Data mining |
dc.subject | Biochemical markers |
dc.subject | Digestive system diseases |
dc.subject | Bioinformatics |
dc.title | From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration |
dc.type | info:eu-repo/semantics/article |
dc.type | info:eu-repo/semantics/publishedVersion |