Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM)

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

2 Citations (Scopus)

Abstract

The Microarrays technology is growing rapidly in bioinformatics. Microarray is a tool for measuring thousands gene expressions level of a sample. Microarray can be used to diagnose cancer including carcinoma. Carcinoma is one of cancer type that originated from epithelial tissue. Microarray data of carcinoma which highly dimensionality would be clustered to help diagnosing carcinoma patients. A highly dimensional data usually need a long computation time. In this paper, carcinoma microarray data would be clustered using spectral clustering method since it had a good capability to reduce data dimension. The result of spectral clustering would be partitioned using Self Organizing Map (SOM) algorithm. SOM is a popular implementation of artificial neural network for clustering. The advantage of SOM algorithm is that it efficiently handle big data and robust to data noise. This research aims to implement spectral clustering and SOM to classify microarray data of carcinoma genes expression from 7457 genes. The result of this study obtained three clusters of carcinoma genes.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • SOM
  • carcinoma
  • microarray
  • spectral clustering

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