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SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 10 No. 2 (2017) · pp. 108–115

DOI
10.21609/jiki.v10i2.481
ISSN
2502-9274, 2088-7051
Published
2017-06-30
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. In this paper several methods of supervised learning (decisien tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain the best method that can be used for gene analysis. In this study there is no optimization and tuning of the algorithm to test the ability of general algorithms. There are 1881 features of microRNA gene epresi on 25 cancer classes based on tissue location. A simple feature selection method is used to test the comparison of the algorithm. Expreriments were conducted with various scenarios to test the accuracy of the classification.

Keywords

Collaboration network of the authors

15 authors · 28 co-authorship links · 17 across different organisations

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  • University of Indonesia
  • Diponegoro University
  • Nagoya University
  • University of Edinburgh
  • Politeknik Negeri Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Muhammad Febrian RachmadiUniversity of Edinburgh2
BudiantoUniversity of Indonesia1
LasguidoUniversity of Indonesia1
Fahry FathurrahmanUniversity of Indonesia1
Muhammad Syaeful FajarUniversity of Indonesia1
Fadhlurrahman AfifUniversity of Indonesia1
Muhammad Anwar Ma’SumUniversity of Indonesia1
Dwi Marhaendro Jati PurnomoUniversity of Indonesia1
Arie Rachmad SyulistyoPoliteknik Negeri Malang1
Ikhsan Wisnuadji GamadarendaDiponegoro University1
Kusworo AdiDiponegoro University1
Naufal BudimanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER</dc:title>
	<dc:creator>Waspada, Indra</dc:creator>
	<dc:creator>Wibowo, Adi</dc:creator>
	<dc:creator>Meraz, Noel Segura</dc:creator>
	<dc:subject xml:lang="en-US">Cancer</dc:subject>
	<dc:subject xml:lang="en-US">MicroRNA</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">Decesion Tree</dc:subject>
	<dc:subject xml:lang="en-US">Naïve Bayes</dc:subject>
	<dc:subject xml:lang="en-US">Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Deep Learning</dc:subject>
	<dc:description xml:lang="en-US">The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. In this paper several methods of supervised learning (decisien tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain the best method that can be used for gene analysis. In this study there is no optimization and tuning of the algorithm to test the ability of general algorithms. There are 1881 features of microRNA gene epresi on 25 cancer classes based on tissue location. A simple feature selection method is used to test the comparison of the algorithm. Expreriments were conducted with various scenarios to test the accuracy of the classification.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2017-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/481</dc:identifier>
	<dc:identifier>10.21609/jiki.v10i2.481</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 10 No. 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 108-115</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v10i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/481/381</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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