A Novel Hypergraph-Based Genetic Algorithm (HGGA) Built on Unimodular and Anti-homomorphism Properties for DNA Sequencing by Hybridization.
Identifieur interne : 000739 ( Main/Exploration ); précédent : 000738; suivant : 000740A Novel Hypergraph-Based Genetic Algorithm (HGGA) Built on Unimodular and Anti-homomorphism Properties for DNA Sequencing by Hybridization.
Auteurs : V. Swaminathan [Inde] ; Gangothri Rajaram [Inde] ; V. Abhishek [Inde] ; Boosi Shashank Reddy [Inde] ; K. Kannan [Inde]Source :
- Interdisciplinary sciences, computational life sciences [ 1867-1462 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : DNA.
- methods : Computational Biology.
- Algorithms, Artificial Intelligence, Big Data, Models, Genetic, Nucleic Acid Hybridization, Sequence Analysis, DNA.
Abstract
The sequencing by hybridization (SBH) of determining the order in which nucleotides should occur on a DNA string is still under discussion for enhancements on computational intelligence although the next generation of DNA sequencing has come into existence. In the last decade, many works related to graph theory-based DNA sequencing have been carried out in the literature. This paper proposes a method for SBH by integrating hypergraph with genetic algorithm (HGGA) for designing a novel analytic technique to obtain DNA sequence from its spectrum. The paper represents elements of the spectrum and its relation as hypergraph and applies the unimodular property to ensure the compatibility of relations between l-mers. The hypergraph representation and unimodular property are bound with the genetic algorithm that has been customized with a novel selection and crossover operator reducing the computational complexity with accelerated convergence. Subsequently, upon determining the primary strand, an anti-homomorphism is invoked to find the reverse complement of the sequence. The proposed algorithm is implemented in the GenBank BioServer datasets, and the results are found to prove the efficiency of the algorithm. The HGGA is a non-classical algorithm with significant advantages and computationally attractive complexity reductions ranging to [Formula: see text] with improved accuracy that makes it prominent for applications other than DNA sequencing like image processing, task scheduling and big data processing.
DOI: 10.1007/s12539-017-0267-y
PubMed: 29110287
Affiliations:
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In the last decade, many works related to graph theory-based DNA sequencing have been carried out in the literature. This paper proposes a method for SBH by integrating hypergraph with genetic algorithm (HGGA) for designing a novel analytic technique to obtain DNA sequence from its spectrum. The paper represents elements of the spectrum and its relation as hypergraph and applies the unimodular property to ensure the compatibility of relations between l-mers. The hypergraph representation and unimodular property are bound with the genetic algorithm that has been customized with a novel selection and crossover operator reducing the computational complexity with accelerated convergence. Subsequently, upon determining the primary strand, an anti-homomorphism is invoked to find the reverse complement of the sequence. The proposed algorithm is implemented in the GenBank BioServer datasets, and the results are found to prove the efficiency of the algorithm. The HGGA is a non-classical algorithm with significant advantages and computationally attractive complexity reductions ranging to [Formula: see text] with improved accuracy that makes it prominent for applications other than DNA sequencing like image processing, task scheduling and big data processing.</div></front></TEI><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Swaminathan, V" sort="Swaminathan, V" uniqKey="Swaminathan V" first="V" last="Swaminathan">V. Swaminathan</name></noRegion><name sortKey="Abhishek, V" sort="Abhishek, V" uniqKey="Abhishek V" first="V" last="Abhishek">V. Abhishek</name><name sortKey="Kannan, K" sort="Kannan, K" uniqKey="Kannan K" first="K" last="Kannan">K. Kannan</name><name sortKey="Rajaram, Gangothri" sort="Rajaram, Gangothri" uniqKey="Rajaram G" first="Gangothri" last="Rajaram">Gangothri Rajaram</name><name sortKey="Reddy, Boosi Shashank" sort="Reddy, Boosi Shashank" uniqKey="Reddy B" first="Boosi Shashank" last="Reddy">Boosi Shashank Reddy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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