Current Challenges for Big Omics Data Analytics and Precision Medicine

Autores
Fernandez, Elmer Andres; Casares, Federico
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Ambitious efforts to characterize disease have been made worldwide, mainly in cancer, with initiatives such as the Cancer Genome Atlas. Many of these cost-intensive studies use cutting-edge technologies to delve deeply into the intrinsic genomic, transcriptomic, proteomic, metabolomic, etc, (i.e., omics) type of data to better explain the phenotype. But while more data is being stored, the complexity of cancer seems to challenge even more our ability to understand its nature and thus to uncover useful bio-physiological information. We strongly believe that data analytics, as well as our understanding of ‘normal’ cases, are still in their infancy, opening great opportunities in translational cancer research to pursue precision medicine through Big Omics Data analytics.
Fil: Fernandez, Elmer Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas. Universidad Católica de Córdoba. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas; Argentina
Fil: Casares, Federico. LISRA Institute; Estados Unidos
Materia
DATA INTERPRETATION, STATISTICAL
GENOMICS
MICROARRAY ANALYSIS
PROTEOMICS
STATISTICS AS TOPIC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/91297
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Current Challenges for Big Omics Data Analytics and Precision MedicineFernandez, Elmer AndresCasares, FedericoDATA INTERPRETATION, STATISTICALGENOMICSMICROARRAY ANALYSISPROTEOMICSSTATISTICS AS TOPIChttps://purl.org/becyt/ford/3.4https://purl.org/becyt/ford/3Ambitious efforts to characterize disease have been made worldwide, mainly in cancer, with initiatives such as the Cancer Genome Atlas. Many of these cost-intensive studies use cutting-edge technologies to delve deeply into the intrinsic genomic, transcriptomic, proteomic, metabolomic, etc, (i.e., omics) type of data to better explain the phenotype. But while more data is being stored, the complexity of cancer seems to challenge even more our ability to understand its nature and thus to uncover useful bio-physiological information. We strongly believe that data analytics, as well as our understanding of ‘normal’ cases, are still in their infancy, opening great opportunities in translational cancer research to pursue precision medicine through Big Omics Data analytics.Fil: Fernandez, Elmer Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas. Universidad Católica de Córdoba. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas; ArgentinaFil: Casares, Federico. LISRA Institute; Estados UnidosInternational Scientific Information, Inc.2018-01-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/91297Fernandez, Elmer Andres; Casares, Federico; Current Challenges for Big Omics Data Analytics and Precision Medicine; International Scientific Information, Inc.; Medical Science & Technology; 16-1-20182329-0072CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.medscitechnol.com/abstract/index/idArt/908220info:eu-repo/semantics/altIdentifier/doi/10.12659/MST.908220info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:48:48Zoai:ri.conicet.gov.ar:11336/91297instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:48:48.301CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Current Challenges for Big Omics Data Analytics and Precision Medicine
title Current Challenges for Big Omics Data Analytics and Precision Medicine
spellingShingle Current Challenges for Big Omics Data Analytics and Precision Medicine
Fernandez, Elmer Andres
DATA INTERPRETATION, STATISTICAL
GENOMICS
MICROARRAY ANALYSIS
PROTEOMICS
STATISTICS AS TOPIC
title_short Current Challenges for Big Omics Data Analytics and Precision Medicine
title_full Current Challenges for Big Omics Data Analytics and Precision Medicine
title_fullStr Current Challenges for Big Omics Data Analytics and Precision Medicine
title_full_unstemmed Current Challenges for Big Omics Data Analytics and Precision Medicine
title_sort Current Challenges for Big Omics Data Analytics and Precision Medicine
dc.creator.none.fl_str_mv Fernandez, Elmer Andres
Casares, Federico
author Fernandez, Elmer Andres
author_facet Fernandez, Elmer Andres
Casares, Federico
author_role author
author2 Casares, Federico
author2_role author
dc.subject.none.fl_str_mv DATA INTERPRETATION, STATISTICAL
GENOMICS
MICROARRAY ANALYSIS
PROTEOMICS
STATISTICS AS TOPIC
topic DATA INTERPRETATION, STATISTICAL
GENOMICS
MICROARRAY ANALYSIS
PROTEOMICS
STATISTICS AS TOPIC
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.4
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Ambitious efforts to characterize disease have been made worldwide, mainly in cancer, with initiatives such as the Cancer Genome Atlas. Many of these cost-intensive studies use cutting-edge technologies to delve deeply into the intrinsic genomic, transcriptomic, proteomic, metabolomic, etc, (i.e., omics) type of data to better explain the phenotype. But while more data is being stored, the complexity of cancer seems to challenge even more our ability to understand its nature and thus to uncover useful bio-physiological information. We strongly believe that data analytics, as well as our understanding of ‘normal’ cases, are still in their infancy, opening great opportunities in translational cancer research to pursue precision medicine through Big Omics Data analytics.
Fil: Fernandez, Elmer Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas. Universidad Católica de Córdoba. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas; Argentina
Fil: Casares, Federico. LISRA Institute; Estados Unidos
description Ambitious efforts to characterize disease have been made worldwide, mainly in cancer, with initiatives such as the Cancer Genome Atlas. Many of these cost-intensive studies use cutting-edge technologies to delve deeply into the intrinsic genomic, transcriptomic, proteomic, metabolomic, etc, (i.e., omics) type of data to better explain the phenotype. But while more data is being stored, the complexity of cancer seems to challenge even more our ability to understand its nature and thus to uncover useful bio-physiological information. We strongly believe that data analytics, as well as our understanding of ‘normal’ cases, are still in their infancy, opening great opportunities in translational cancer research to pursue precision medicine through Big Omics Data analytics.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-16
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/91297
Fernandez, Elmer Andres; Casares, Federico; Current Challenges for Big Omics Data Analytics and Precision Medicine; International Scientific Information, Inc.; Medical Science & Technology; 16-1-2018
2329-0072
CONICET Digital
CONICET
url http://hdl.handle.net/11336/91297
identifier_str_mv Fernandez, Elmer Andres; Casares, Federico; Current Challenges for Big Omics Data Analytics and Precision Medicine; International Scientific Information, Inc.; Medical Science & Technology; 16-1-2018
2329-0072
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.medscitechnol.com/abstract/index/idArt/908220
info:eu-repo/semantics/altIdentifier/doi/10.12659/MST.908220
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv International Scientific Information, Inc.
publisher.none.fl_str_mv International Scientific Information, Inc.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.25334

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