Performance analysis of the Survival-SVM classifier applied to gene-expression databases

The analysis of epigenetic information for the diagnosis and prognosis of patients has been gaining relevance in recent years due to the technological progress that entails a decrease in information extraction and processing costs. One of the tasks most commonly carried out in this area is obtainin...

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Detalles Bibliográficos
Autores principales: Camele, Genaro, Hasperué, Waldo
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2023
Materias:
Ciencias Informáticas
Survival analysis
Survival Support Vector Machines
Regression, Performance
Apache Spark
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/164807
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-164807
record_format dspace
spelling I19-R120-10915-1648072024-04-15T20:03:21Z http://sedici.unlp.edu.ar/handle/10915/164807 Performance analysis of the Survival-SVM classifier applied to gene-expression databases Camele, Genaro Hasperué, Waldo 2023-10 2024 2024-04-15T13:27:41Z es Ciencias Informáticas Survival analysis Survival Support Vector Machines Regression, Performance Apache Spark The analysis of epigenetic information for the diagnosis and prognosis of patients has been gaining relevance in recent years due to the technological progress that entails a decrease in information extraction and processing costs. One of the tasks most commonly carried out in this area is obtaining models that allow using patient epigenetic information to make inferences about survival analysis. As a result, optimizing these models turns into a problem of great interest today. In this article, the evaluation of different metrics and execution times for the Survival Support Vector Machines model is carried out through survival analysis applied to gene expression databases. Different experiments were performed varying the number of genes used for training to measure the correlation between model performance and data growth. The results showed that linear and polynomial kernels offer a better balance between execution time and model predictive power when the number of genes to be evaluated is less than 2000, while the cosine and RBF kernels are better candidates otherwise. Instituto de Investigación en Informática Red de Universidades con Carreras en Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 97-105
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Survival analysis
Survival Support Vector Machines
Regression, Performance
Apache Spark
spellingShingle Ciencias Informáticas
Survival analysis
Survival Support Vector Machines
Regression, Performance
Apache Spark
Camele, Genaro
Hasperué, Waldo
Performance analysis of the Survival-SVM classifier applied to gene-expression databases
topic_facet Ciencias Informáticas
Survival analysis
Survival Support Vector Machines
Regression, Performance
Apache Spark
description The analysis of epigenetic information for the diagnosis and prognosis of patients has been gaining relevance in recent years due to the technological progress that entails a decrease in information extraction and processing costs. One of the tasks most commonly carried out in this area is obtaining models that allow using patient epigenetic information to make inferences about survival analysis. As a result, optimizing these models turns into a problem of great interest today. In this article, the evaluation of different metrics and execution times for the Survival Support Vector Machines model is carried out through survival analysis applied to gene expression databases. Different experiments were performed varying the number of genes used for training to measure the correlation between model performance and data growth. The results showed that linear and polynomial kernels offer a better balance between execution time and model predictive power when the number of genes to be evaluated is less than 2000, while the cosine and RBF kernels are better candidates otherwise.
format Objeto de conferencia
Objeto de conferencia
author Camele, Genaro
Hasperué, Waldo
author_facet Camele, Genaro
Hasperué, Waldo
author_sort Camele, Genaro
title Performance analysis of the Survival-SVM classifier applied to gene-expression databases
title_short Performance analysis of the Survival-SVM classifier applied to gene-expression databases
title_full Performance analysis of the Survival-SVM classifier applied to gene-expression databases
title_fullStr Performance analysis of the Survival-SVM classifier applied to gene-expression databases
title_full_unstemmed Performance analysis of the Survival-SVM classifier applied to gene-expression databases
title_sort performance analysis of the survival-svm classifier applied to gene-expression databases
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/164807
work_keys_str_mv AT camelegenaro performanceanalysisofthesurvivalsvmclassifierappliedtogeneexpressiondatabases
AT hasperuewaldo performanceanalysisofthesurvivalsvmclassifierappliedtogeneexpressiondatabases
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