Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)

Clustering of genes on the basis of expression profiles is usually taken as a first step in understanding how a class of genes acts in consort during a biological process. The fundamental premise for applying such methods is that genes with similar functi

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Bibliographic Details
Main Author:	JELLY, AUREUS
Format:	text
Published:	Archīum Ateneo 2013
Subjects:	Cluster set theory Computer algorithms
Online Access:	https://archium.ateneo.edu/theses-dissertations/221
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Institution:	Ateneo De Manila University

id	ph-ateneo-arc.theses-dissertations-1347
record_format	eprints
spelling	ph-ateneo-arc.theses-dissertations-13472021-04-11T05:42:03Z Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) JELLY, AUREUS Clustering of genes on the basis of expression profiles is usually taken as a first step in understanding how a class of genes acts in consort during a biological process. The fundamental premise for applying such methods is that genes with similar functi 2013-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/221 Theses and Dissertations (All) Archīum Ateneo Cluster set theory Computer algorithms
institution	Ateneo De Manila University
building	Ateneo De Manila University Library
continent	Asia
country	Philippines Philippines
content_provider	Ateneo De Manila University Library
collection	archium.Ateneo Institutional Repository
topic	Cluster set theory Computer algorithms
spellingShingle	Cluster set theory Computer algorithms JELLY, AUREUS Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
description	Clustering of genes on the basis of expression profiles is usually taken as a first step in understanding how a class of genes acts in consort during a biological process. The fundamental premise for applying such methods is that genes with similar functi
format	text
author	JELLY, AUREUS
author_facet	JELLY, AUREUS
author_sort	JELLY, AUREUS
title	Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
title_short	Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
title_full	Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
title_fullStr	Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
title_full_unstemmed	Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)
title_sort	modifying the k-medoid algorithm to improve gene expression data clustering using biological homogeneity index (bhi) and biological stability index (bsi)
publisher	Archīum Ateneo
publishDate	2013
url	https://archium.ateneo.edu/theses-dissertations/221
_version_	1712577808364994560

Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)

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