Identifying the best metrics to find the best quality clusters of genes from gene expression data

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

書誌詳細
主要な著者:	Choudhury, Joydhriti, Roshni, Tanzima Rahman, Chowdhury, Md. Tawhidul Islam, Rayon, Raihanoor Reza
その他の著者:	Mottalib, Md. Abdul
フォーマット:	学位論文
言語:	English
出版事項:	Brac University 2019
主題:	Bioinformatics Microarray Gene expression Phylogenetic tree Hierarchical clustering Distance metric Linkage method Cluster analysis.
オンライン･アクセス:	http://hdl.handle.net/10361/12810

id	10361-12810
record_format	dspace
spelling	10361-128102022-01-26T10:18:27Z Identifying the best metrics to find the best quality clusters of genes from gene expression data Choudhury, Joydhriti Roshni, Tanzima Rahman Chowdhury, Md. Tawhidul Islam Rayon, Raihanoor Reza Mottalib, Md. Abdul Ajwad, Ajwad Department of Computer Science and Engineering, Brac University Bioinformatics Microarray Gene expression Phylogenetic tree Hierarchical clustering Distance metric Linkage method Cluster analysis. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-40). Microarray data is used to create groups of similar genes based on their phenotypic attributes. Information extracted from these groups of gene can be applied to path- way analysis, disease predictions, target identification in drug design and many other important applications and functionalities in biology. However, how to determine a distance metric to measure the similarities among genes has always been a great chal- lenge. In our work, we have studied sixteen combination of distance-linkage combina- tional metrics and tried to and the groups of similar genes based on their expression level by building phylogenetic tree. Furthermore, to validate our endings we have evaluate the output of the same trails on three different datasets. Our work suggests that, Maximum distance metric with the combination of Average linkage metrics gives the optimal quality while grouping similar genes together by building a phylogenetic tree. Joydhriti Choudhury Tanzima Rahman Roshni Md. Tawhidul Islam Chowdhury Raihanoor Reza Rayon B. Computer Science 2019-10-28T04:04:38Z 2019-10-28T04:04:38Z 2019 2019-04 Thesis ID 15301125 ID 15301125 ID 16101321 ID 18141021 http://hdl.handle.net/10361/12810 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 65 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Bioinformatics Microarray Gene expression Phylogenetic tree Hierarchical clustering Distance metric Linkage method Cluster analysis.
spellingShingle	Bioinformatics Microarray Gene expression Phylogenetic tree Hierarchical clustering Distance metric Linkage method Cluster analysis. Choudhury, Joydhriti Roshni, Tanzima Rahman Chowdhury, Md. Tawhidul Islam Rayon, Raihanoor Reza Identifying the best metrics to find the best quality clusters of genes from gene expression data
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Mottalib, Md. Abdul
author_facet	Mottalib, Md. Abdul Choudhury, Joydhriti Roshni, Tanzima Rahman Chowdhury, Md. Tawhidul Islam Rayon, Raihanoor Reza
format	Thesis
author	Choudhury, Joydhriti Roshni, Tanzima Rahman Chowdhury, Md. Tawhidul Islam Rayon, Raihanoor Reza
author_sort	Choudhury, Joydhriti
title	Identifying the best metrics to find the best quality clusters of genes from gene expression data
title_short	Identifying the best metrics to find the best quality clusters of genes from gene expression data
title_full	Identifying the best metrics to find the best quality clusters of genes from gene expression data
title_fullStr	Identifying the best metrics to find the best quality clusters of genes from gene expression data
title_full_unstemmed	Identifying the best metrics to find the best quality clusters of genes from gene expression data
title_sort	identifying the best metrics to find the best quality clusters of genes from gene expression data
publisher	Brac University
publishDate	2019
url	http://hdl.handle.net/10361/12810
work_keys_str_mv	AT choudhuryjoydhriti identifyingthebestmetricstofindthebestqualityclustersofgenesfromgeneexpressiondata AT roshnitanzimarahman identifyingthebestmetricstofindthebestqualityclustersofgenesfromgeneexpressiondata AT chowdhurymdtawhidulislam identifyingthebestmetricstofindthebestqualityclustersofgenesfromgeneexpressiondata AT rayonraihanoorreza identifyingthebestmetricstofindthebestqualityclustersofgenesfromgeneexpressiondata
_version_	1814308885383610368

Identifying the best metrics to find the best quality clusters of genes from gene expression data

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