Integration and comparison of different genomic data for outcome prediction in cancer

Hdl Handle:
http://hdl.handle.net/11285/613678
Title:
Integration and comparison of different genomic data for outcome prediction in cancer
Authors:
Gomez Rueda, Hugo ( 0000-0002-4702-6764 ) ; Martínez Ledesma, Emmanuel; Martínez Torteya, Antonio; Palacios Corona, Rebeca; Treviño, Victor
Affiliation:
Tecnologico de Monterrey
Issue Date:
2005-10-29
Publisher:
Open Access Publisher
Abstract:
In cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent. Therefore, our purpose is to characterize the prognostic power of models obtained from different genomic data types, cancer types, and algorithms. For this, we compared the prognostic power using the concordance and prognostic index of models obtained from EXPR, MIRNA, CNA, MUT data and their integration for ovarian serous cystadenocarcinoma (OV), multiform glioblastoma (GBM), lung adenocarcinoma (LUAD), and breast cancer (BRCA) datasets from The Cancer Genome Atlas repository. We used three different algorithms for prognostic model selection based on constrained particle swarm optimization (CPSO), network feature selection (NFS), and least absolute shrinkage and selection operator (LASSO).
Discipline:
Ciencias de la Salud / Health Sciences
Additional Links:
https://biodatamining.biomedcentral.com/articles/10.1186/s13040-015-0065-1
Type:
Artículo / Article
Series/Report no.:
Survival; Cancer; Genomics; TCGA
Appears in Collections:
Springer/BMC

Full metadata record

DC FieldValue Language
dc.contributor.authorGomez Rueda, Hugoen
dc.contributor.authorMartínez Ledesma, Emmanuelen
dc.contributor.authorMartínez Torteya, Antonioen
dc.contributor.authorPalacios Corona, Rebecaen
dc.contributor.authorTreviño, Victoren
dc.date.accessioned2016-06-20T03:09:16Z-
dc.date.available2016-06-20T03:09:16Z-
dc.date.issued2005-10-29-
dc.identifier.otherBioData Mining-
dc.identifier.urihttp://hdl.handle.net/11285/613678-
dc.description.abstractIn cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent. Therefore, our purpose is to characterize the prognostic power of models obtained from different genomic data types, cancer types, and algorithms. For this, we compared the prognostic power using the concordance and prognostic index of models obtained from EXPR, MIRNA, CNA, MUT data and their integration for ovarian serous cystadenocarcinoma (OV), multiform glioblastoma (GBM), lung adenocarcinoma (LUAD), and breast cancer (BRCA) datasets from The Cancer Genome Atlas repository. We used three different algorithms for prognostic model selection based on constrained particle swarm optimization (CPSO), network feature selection (NFS), and least absolute shrinkage and selection operator (LASSO).en
dc.language.isoenen
dc.publisherOpen Access Publisheren
dc.relation.ispartofseriesSurvivalen
dc.relation.ispartofseriesCanceren
dc.relation.ispartofseriesGenomicsen
dc.relation.ispartofseriesTCGAen
dc.relation.urlhttps://biodatamining.biomedcentral.com/articles/10.1186/s13040-015-0065-1en
dc.rightsOpen Access-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleIntegration and comparison of different genomic data for outcome prediction in canceren
dc.typeArtículo / Articleen
dc.contributor.departmentTecnologico de Monterreyen
dc.subject.disciplineCiencias de la Salud / Health Sciences-
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