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

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 gen...

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Detalles Bibliográficos
Autores principales: Palazzo, Martin, Beauseroy, Pierre, Yankilevich, Patricio
Formato: Articulo
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/135036
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/83
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-135036
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
spellingShingle Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
Palazzo, Martin
Beauseroy, Pierre
Yankilevich, Patricio
Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
topic_facet Ciencias Informáticas
Ciencias Médicas
Feature selection
Kernel Learning
Cancer Genomics
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.
format Articulo
Articulo
author Palazzo, Martin
Beauseroy, Pierre
Yankilevich, Patricio
author_facet Palazzo, Martin
Beauseroy, Pierre
Yankilevich, Patricio
author_sort Palazzo, Martin
title Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models
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
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/135036
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/83
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AT beauseroypierre hepatocellularcarcinomatumorstageclassificationandgeneselectionusingmachinelearningmodels
AT yankilevichpatricio hepatocellularcarcinomatumorstageclassificationandgeneselectionusingmachinelearningmodels
bdutipo_str Repositorios
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