Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction

In the first experiment of Deoxyribonucleic Acid (DNA) computation, Adleman has solved a seven nodes Hamiltonian Path Problem (HPP) by applying some biotechnology techniques such as hybridization and polymerase chain reaction (PCR). In that experiment, graduated PCR has been used to visualize the Ha...

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Main Author:	Mohamed Saaid, Muhammad Faiz
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/18194/1/MuhammadFaizMohamedSaaidMFKE2009.pdf
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id	my-utm-ep.18194
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spelling	my-utm-ep.181942018-06-04T09:54:20Z Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction 2009-07 Mohamed Saaid, Muhammad Faiz QA Mathematics TK Electrical engineering. Electronics Nuclear engineering In the first experiment of Deoxyribonucleic Acid (DNA) computation, Adleman has solved a seven nodes Hamiltonian Path Problem (HPP) by applying some biotechnology techniques such as hybridization and polymerase chain reaction (PCR). In that experiment, graduated PCR has been used to visualize the Hamiltonian path. In other research work, a novel readout method tailored specifically to the HPP in DNA computing was proposed, which employs a hybrid in vitro-in silico approach. In the in vitro phase, TaqMan-based real-time PCR reactions are performed in parallel, to investigate the ordering of pairs of nodes in the Hamiltonian path, in terms of relative distance from the DNA sequence encoding the known start node. The resulting relative orderings are then processed in silico, which efficiently returns the complete Hamiltonian path. However, this method used manual classification to distinguish the two different reactions of real-time PCR. In this thesis, clustering techniques are implemented during the in silico phase. Clustering is crucial to identify automatically two different reactions produced by real-time PCR. K-means, Fuzzy C-means (FCM), and Alternative Fuzzy C-means (AFCM) clustering algorithms are implemented to differentiate the output of realtime PCR. Results show that K-means and FCM clustering algorithms are capable to classify the two different reactions of real-time PCR. In addition, it has been shown that AFCM clustering algorithm is better than FCM and K-means in term of handling outliers in the real-time PCR output data. Application of clustering techniques have improved the in silico information processing of the readout method. 2009-07 Thesis http://eprints.utm.my/id/eprint/18194/ http://eprints.utm.my/id/eprint/18194/1/MuhammadFaizMohamedSaaidMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics QA Mathematics
spellingShingle	QA Mathematics QA Mathematics Mohamed Saaid, Muhammad Faiz Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
description	In the first experiment of Deoxyribonucleic Acid (DNA) computation, Adleman has solved a seven nodes Hamiltonian Path Problem (HPP) by applying some biotechnology techniques such as hybridization and polymerase chain reaction (PCR). In that experiment, graduated PCR has been used to visualize the Hamiltonian path. In other research work, a novel readout method tailored specifically to the HPP in DNA computing was proposed, which employs a hybrid in vitro-in silico approach. In the in vitro phase, TaqMan-based real-time PCR reactions are performed in parallel, to investigate the ordering of pairs of nodes in the Hamiltonian path, in terms of relative distance from the DNA sequence encoding the known start node. The resulting relative orderings are then processed in silico, which efficiently returns the complete Hamiltonian path. However, this method used manual classification to distinguish the two different reactions of real-time PCR. In this thesis, clustering techniques are implemented during the in silico phase. Clustering is crucial to identify automatically two different reactions produced by real-time PCR. K-means, Fuzzy C-means (FCM), and Alternative Fuzzy C-means (AFCM) clustering algorithms are implemented to differentiate the output of realtime PCR. Results show that K-means and FCM clustering algorithms are capable to classify the two different reactions of real-time PCR. In addition, it has been shown that AFCM clustering algorithm is better than FCM and K-means in term of handling outliers in the real-time PCR output data. Application of clustering techniques have improved the in silico information processing of the readout method.
format	Thesis
qualification_level	Master's degree
author	Mohamed Saaid, Muhammad Faiz
author_facet	Mohamed Saaid, Muhammad Faiz
author_sort	Mohamed Saaid, Muhammad Faiz
title	Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
title_short	Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
title_full	Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
title_fullStr	Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
title_full_unstemmed	Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction
title_sort	clustering techniques for dna computing readout method based on real-time polymerase chain reaction
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/18194/1/MuhammadFaizMohamedSaaidMFKE2009.pdf
_version_	1747815215631695872

Clustering techniques for DNA computing readout method based on real-time polymerase chain reaction

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