Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1475-2840
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics ; volume:18 ; number:1 ; day:11 ; month:6 ; year:2019 ; pages:1-16 ; date:12.2019
Cardiovascular diabetology ; 18, Heft 1 (11.6.2019), 1-16, 12.2019
- Urheber
-
Hathaway, Quincy A.
Roth, Skyler M.
Pinti, Mark V.
Sprando, Daniel C.
Kunovac, Amina
Durr, Andrya J.
Cook, Chris C.
Fink, Garrett K.
Cheuvront, Tristen B.
Grossman, Jasmine H.
Aljahli, Ghadah A.
Taylor, Andrew D.
Giromini, Andrew P.
Allen, Jessica L.
Hollander, John M.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12933-019-0879-0
- URN
-
urn:nbn:de:101:1-2019110505171019751392
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:20 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Hathaway, Quincy A.
- Roth, Skyler M.
- Pinti, Mark V.
- Sprando, Daniel C.
- Kunovac, Amina
- Durr, Andrya J.
- Cook, Chris C.
- Fink, Garrett K.
- Cheuvront, Tristen B.
- Grossman, Jasmine H.
- Aljahli, Ghadah A.
- Taylor, Andrew D.
- Giromini, Andrew P.
- Allen, Jessica L.
- Hollander, John M.
- SpringerLink (Online service)
Ähnliche Objekte (12)
Functional & integrative genomics
Genomics Portals: integrative web-platform for mining genomics data
Integrative relational machine-learning for understanding drug side-effect profiles
Biomarkers of diabetic kidney disease
Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis
Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
Integrative functional genomics decodes herpes simplex virus 1
Integrative analysis of neuroblastoma and pheochromocytoma genomics data
Integrative network-based Bayesian analysis of diverse genomics data
New urinary biomarkers for diabetic kidney disease
Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations
Potential clinical biomarkers and perspectives in diabetic cardiomyopathy
Functional & integrative genomics
Genomics Portals: integrative web-platform for mining genomics data
Integrative relational machine-learning for understanding drug side-effect profiles
Biomarkers of diabetic kidney disease
Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis
Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
Integrative functional genomics decodes herpes simplex virus 1
Integrative analysis of neuroblastoma and pheochromocytoma genomics data
Integrative network-based Bayesian analysis of diverse genomics data
New urinary biomarkers for diabetic kidney disease
Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations
Potential clinical biomarkers and perspectives in diabetic cardiomyopathy
Functional & integrative genomics
Genomics Portals: integrative web-platform for mining genomics data
Integrative relational machine-learning for understanding drug side-effect profiles
Biomarkers of diabetic kidney disease
Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis
Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
Integrative functional genomics decodes herpes simplex virus 1
Integrative analysis of neuroblastoma and pheochromocytoma genomics data
Integrative network-based Bayesian analysis of diverse genomics data
New urinary biomarkers for diabetic kidney disease
Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations