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Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 2 (2018) · pp. 123–137

DOI
10.5614/itbj.ict.res.appl.2018.12.2.2
ISSN
2338-5499, 2337-5787
Published
2018-09-28
Publisher
DRI - ITB
Language
eng

Abstract

K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time.

Keywords

Collaboration network of the authors

29 authors · 49 co-authorship links · 7 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • IPB University
  • Universitas Kristen Wira Wacana Sumba
  • Universitas Muhammadiyah Parepare
  • Pakuan University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Annisa AnnisaIPB University2
Mushthofa MushthofaIPB University2
IrmansyahAffiliation unknown1
Dhany Nugraha RamdhanyAffiliation unknown1
Aziz KustiyoAffiliation unknown1
Ekowati HandharyaniIPB University1
Mustakim MustakimUniversitas Muhammadiyah Parepare1
Irman HermadiIPB University1
Albert AdrianusIPB University1
Larasati LarasatiIPB University1
Nabila Sekar RamadhantiIPB University1
Siti RaehanIPB University1
Medria Kusuma Dewi HardhienataAffiliation unknown1
Sri NurdiatiIPB University1
Fajar Delli WihartikoPakuan University1
Edi SantosaIPB University1
Muh Fadhil Al-Haaq GinogaIPB University1
Hendra RahmawanIPB University1
Refianto Damai DarmawanIPB University1
Bambang Hadi PurnomoAffiliation unknown1
Heru SukocoAffiliation unknown1
Ivan YustiavandanaAffiliation unknown1
Jemsri Stenli BatlajeryAffiliation unknown1
Arif Imam SurosoIPB University1
Popong NurhayatiAffiliation unknown1
Trukan Sri BahukelingAffiliation 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://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification</dc:title>
	<dc:creator>Pekuwali, Arini</dc:creator>
	<dc:creator>Kusuma, Wisnu Ananta</dc:creator>
	<dc:creator>Buono, Agus</dc:creator>
	<dc:subject xml:lang="en-US">genetic algorithm</dc:subject>
	<dc:subject xml:lang="en-US">k-mers</dc:subject>
	<dc:subject xml:lang="en-US">metagenome</dc:subject>
	<dc:subject xml:lang="en-US">naïve Bayesian classifier</dc:subject>
	<dc:subject xml:lang="en-US">spaced k-mers</dc:subject>
	<dc:description xml:lang="en-US">K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-09-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/3228</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.2.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 2 (2018); 123-137</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2018.12.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/3228/3463</dc:relation>
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