Algorithms for hierarchical clustering of gene expression data Komarina, Srikanth
dc.contributor.department Electrical and Computer Engineering 2020-11-09T01:16:48.000 2021-02-26T08:56:46Z 2021-02-26T08:56:46Z Thu Jan 01 00:00:00 UTC 2004 2004-01-01
dc.description.abstract <p>Genes are parts of the genome which encode for proteins in an organism. Proteins play an important part in many biologicl processes in any organism. Measuring expression level of a gene helps biologists estimate the amount of protein produced by that gene. Microarrays can be used to measure the expression levels of thousands of genes in a single experiment. Using additional techniques such as clustering various correlations among genes of interest can be found. The most commonly used clustering technique for microarray data analysis is hierarchical clustering. Various metrics such ad Euclidean, Manhattan, Pearson correlation coefficient have been used to measure (dis)similarity between genes. A commonly used software for hierarchical clustering based on Pearson correlation coefficient takes O(N[Arrow pointing up]3) for clustering N genes, even though there are algorithms which can reduce the runtime to O(N[Arrow pointing up]2). In this thesis, we show how the runtime can be reduced to O(N log N) by using a geometric interpretation of the Pearson correlation coeffcient and show that it is optimal.</p>
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dc.identifier.articleid 21678
dc.identifier.contextkey 20115128
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dc.identifier.submissionpath rtd/20679
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:26:59 UTC 2022
dc.subject.keywords Electrical and computer engineering
dc.subject.keywords Computer engineering
dc.title Algorithms for hierarchical clustering of gene expression data
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff Computer Engineering thesis Master of Science
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