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

A. Bustamam; M. A. Rivai; T. Siswantining

doi:10.1063/1.5064237

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

A. Bustamam, M. A. Rivai, T. Siswantining

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
Editors	Ratna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735417410
DOIs	https://doi.org/10.1063/1.5064237
Publication status	Published - 22 Oct 2018
Event	3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia Duration: 26 Jul 2017 → 27 Jul 2017

Publication series

Name	AIP Conference Proceedings
Volume	2023
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
Country/Territory	Indonesia
City	Bali
Period	26/07/17 → 27/07/17

Keywords

SOM
carcinoma
microarray
spectral clustering

Access to Document

10.1063/1.5064237

Cite this

Bustamam, A., Rivai, M. A., & Siswantining, T. (2018). Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM). In R. Yuniati, T. Mart, I. T. Anggraningrum, D. Triyono, & K. A. Sugeng (Eds.), Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 Article 020240 (AIP Conference Proceedings; Vol. 2023). American Institute of Physics Inc.. https://doi.org/10.1063/1.5064237

Bustamam, A. ; Rivai, M. A. ; Siswantining, T. / Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM). Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017. editor / Ratna Yuniati ; Terry Mart ; Ivandini T. Anggraningrum ; Djoko Triyono ; Kiki A. Sugeng. American Institute of Physics Inc., 2018. (AIP Conference Proceedings).

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title = "Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM)",

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.",

keywords = "SOM, carcinoma, microarray, spectral clustering",

author = "A. Bustamam and Rivai, {M. A.} and T. Siswantining",

note = "Funding Information: This research was supported by HIBAH PUPT 2017 Research grant from Ministry of Research, Technology and Higher Education Republic of Indonesia. Publisher Copyright: {\textcopyright} 2018 Author(s).; 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 ; Conference date: 26-07-2017 Through 27-07-2017",

year = "2018",

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doi = "10.1063/1.5064237",

language = "English",

series = "AIP Conference Proceedings",

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Bustamam, A, Rivai, MA & Siswantining, T 2018, Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM). in R Yuniati, T Mart, IT Anggraningrum, D Triyono & KA Sugeng (eds), Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017., 020240, AIP Conference Proceedings, vol. 2023, American Institute of Physics Inc., 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017, Bali, Indonesia, 26/07/17. https://doi.org/10.1063/1.5064237

Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM). / Bustamam, A.; Rivai, M. A.; Siswantining, T.
Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017. ed. / Ratna Yuniati; Terry Mart; Ivandini T. Anggraningrum; Djoko Triyono; Kiki A. Sugeng. American Institute of Physics Inc., 2018. 020240 (AIP Conference Proceedings; Vol. 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM)

AU - Bustamam, A.

AU - Rivai, M. A.

AU - Siswantining, T.

N1 - Funding Information: This research was supported by HIBAH PUPT 2017 Research grant from Ministry of Research, Technology and Higher Education Republic of Indonesia. Publisher Copyright: © 2018 Author(s).

PY - 2018/10/22

Y1 - 2018/10/22

N2 - 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.

AB - 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.

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KW - microarray

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T3 - AIP Conference Proceedings

BT - Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017

A2 - Yuniati, Ratna

A2 - Mart, Terry

A2 - Anggraningrum, Ivandini T.

A2 - Triyono, Djoko

A2 - Sugeng, Kiki A.

PB - American Institute of Physics Inc.

T2 - 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017

Y2 - 26 July 2017 through 27 July 2017

ER -

Bustamam A, Rivai MA, Siswantining T. Implementation of spectral clustering on microarray data of carcinoma using self organizing map (SOM). In Yuniati R, Mart T, Anggraningrum IT, Triyono D, Sugeng KA, editors, Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017. American Institute of Physics Inc. 2018. 020240. (AIP Conference Proceedings). doi: 10.1063/1.5064237

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

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