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Genetic algorithm solution for double digest problem

The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!)...

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Detalles Bibliográficos
Autores principales: Ganjtabesh, Mohammad, Ahrabian, H, Nowzari-Dalini, A, Kashani Moghadam, Z Razaghi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3374354/
https://www.ncbi.nlm.nih.gov/pubmed/22715298
http://dx.doi.org/10.6026/97320630008453
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author Ganjtabesh, Mohammad
Ahrabian, H
Nowzari-Dalini, A
Kashani Moghadam, Z Razaghi
author_facet Ganjtabesh, Mohammad
Ahrabian, H
Nowzari-Dalini, A
Kashani Moghadam, Z Razaghi
author_sort Ganjtabesh, Mohammad
collection PubMed
description The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data.
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spelling pubmed-33743542012-06-19 Genetic algorithm solution for double digest problem Ganjtabesh, Mohammad Ahrabian, H Nowzari-Dalini, A Kashani Moghadam, Z Razaghi Bioinformation Hypothesis The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data. Biomedical Informatics 2012-05-31 /pmc/articles/PMC3374354/ /pubmed/22715298 http://dx.doi.org/10.6026/97320630008453 Text en © 2012 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Hypothesis
Ganjtabesh, Mohammad
Ahrabian, H
Nowzari-Dalini, A
Kashani Moghadam, Z Razaghi
Genetic algorithm solution for double digest problem
title Genetic algorithm solution for double digest problem
title_full Genetic algorithm solution for double digest problem
title_fullStr Genetic algorithm solution for double digest problem
title_full_unstemmed Genetic algorithm solution for double digest problem
title_short Genetic algorithm solution for double digest problem
title_sort genetic algorithm solution for double digest problem
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3374354/
https://www.ncbi.nlm.nih.gov/pubmed/22715298
http://dx.doi.org/10.6026/97320630008453
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