Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models

Autores
Palazzo, Martin; Beauseroy, Pierre; Yankilevich, Patricio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Cancer researchers are facing the opportunity to analyze and learn from big quantities of omic profiles of tumor samples. Different omic data is now available in several databases and the bioinformatics data analysis and interpretation are current bottlenecks. In this study somatic mutations and gene expression data from Hepatocellular carcinoma tumor samples are used to discriminate by Kernel Learning between tumor subtypes and early and late stages. This classification will allow medical doctors to establish an appropriate treatment according to the tumor stage. By building kernel machines we could discriminate both classes with an acceptable classification accuracy. Feature selection have been implemented to select the key genes which differential expression improves the separability between the samples of early and late stages.
Special Issue dedicated to JAIIO 2018 (Jornadas Argentinas de Informática).
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/135036
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/135036
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning modelsPalazzo, MartinBeauseroy, PierreYankilevich, PatricioCiencias InformáticasCiencias MédicasFeature selectionKernel LearningCancer GenomicsCancer researchers are facing the opportunity to analyze and learn from big quantities of omic profiles of tumor samples. Different omic data is now available in several databases and the bioinformatics data analysis and interpretation are current bottlenecks. In this study somatic mutations and gene expression data from Hepatocellular carcinoma tumor samples are used to discriminate by Kernel Learning between tumor subtypes and early and late stages. This classification will allow medical doctors to establish an appropriate treatment according to the tumor stage. By building kernel machines we could discriminate both classes with an acceptable classification accuracy. Feature selection have been implemented to select the key genes which differential expression improves the separability between the samples of early and late stages.Special Issue dedicated to JAIIO 2018 (Jornadas Argentinas de Informática).Sociedad Argentina de Informática e Investigación Operativa2019-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf26-42http://sedici.unlp.edu.ar/handle/10915/135036enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/83info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:25:51Zoai:sedici.unlp.edu.ar:10915/135036Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:25:51.516SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
title Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
spellingShingle Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
Palazzo, Martin
Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
title_short Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
title_full Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
title_fullStr Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
title_full_unstemmed Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
title_sort Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
dc.creator.none.fl_str_mv Palazzo, Martin
Beauseroy, Pierre
Yankilevich, Patricio
author Palazzo, Martin
author_facet Palazzo, Martin
Beauseroy, Pierre
Yankilevich, Patricio
author_role author
author2 Beauseroy, Pierre
Yankilevich, Patricio
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
topic Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
dc.description.none.fl_txt_mv Cancer researchers are facing the opportunity to analyze and learn from big quantities of omic profiles of tumor samples. Different omic data is now available in several databases and the bioinformatics data analysis and interpretation are current bottlenecks. In this study somatic mutations and gene expression data from Hepatocellular carcinoma tumor samples are used to discriminate by Kernel Learning between tumor subtypes and early and late stages. This classification will allow medical doctors to establish an appropriate treatment according to the tumor stage. By building kernel machines we could discriminate both classes with an acceptable classification accuracy. Feature selection have been implemented to select the key genes which differential expression improves the separability between the samples of early and late stages.
Special Issue dedicated to JAIIO 2018 (Jornadas Argentinas de Informática).
Sociedad Argentina de Informática e Investigación Operativa
description Cancer researchers are facing the opportunity to analyze and learn from big quantities of omic profiles of tumor samples. Different omic data is now available in several databases and the bioinformatics data analysis and interpretation are current bottlenecks. In this study somatic mutations and gene expression data from Hepatocellular carcinoma tumor samples are used to discriminate by Kernel Learning between tumor subtypes and early and late stages. This classification will allow medical doctors to establish an appropriate treatment according to the tumor stage. By building kernel machines we could discriminate both classes with an acceptable classification accuracy. Feature selection have been implemented to select the key genes which differential expression improves the separability between the samples of early and late stages.
publishDate 2019
dc.date.none.fl_str_mv 2019-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/135036
url http://sedici.unlp.edu.ar/handle/10915/135036
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/83
info:eu-repo/semantics/altIdentifier/issn/1514-6774
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
26-42
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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