Data integration model for cancer subtype identification using kernel dimensionality reduction-support vector machine (KDR-SVM)

Ito Wasito, Aulia N. Istiqlal, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper, an integration model of cancer patients data types such as microarray DNA and clinical data will be experimentally explored. The data of integration will be used for cancer subtype identification using kernel based classification methods which is the extension of Support Vector Machine (SVM) approach with Kernel Dimensionality Reduction (KDR). KDR-SVM method will be implemented in Lymphoma cancer database and the relevant clinical information. Data type representation will be modeled in an appropriate kernel matrix. The results of the experiment show that the KDR-10 dimensions and data integration can improve the accuracy of the identification of subtype cancer.

Original languageEnglish
Title of host publicationProceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
Pages876-880
Number of pages5
Publication statusPublished - 1 Dec 2012
Event2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012 - Seoul, Korea, Republic of
Duration: 3 Dec 20125 Dec 2012

Publication series

NameProceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012

Conference

Conference2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period3/12/125/12/12

Keywords

  • Clinical Data
  • Data Integration
  • Kernel Dimensionality Reduction
  • Kernel Matrix
  • Microarray data
  • Support Vector Machine

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