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dc.contributor.authorLee, SX
dc.contributor.authorNg, SK
dc.contributor.authorMcLachlan, GJ
dc.contributor.editorSrinivasa Rao, Arni S.R.
dc.contributor.editorPyne, Saumyadipta
dc.contributor.editorRao, C. R.
dc.date.accessioned2018-10-12T01:34:23Z
dc.date.available2018-10-12T01:34:23Z
dc.date.issued2017
dc.identifier.isbn9780444639684
dc.identifier.doi10.1016/bs.host.2017.08.005
dc.identifier.urihttp://hdl.handle.net/10072/371569
dc.description.abstractMixture models are powerful tools for density estimation and cluster and discriminant analyses. They have enjoyed widespread popularity in biostatistics, biomedicine, medical imaging, and genetics, among many other applied fields. The mixture model framework provides a formal but convenient and flexible approach to model complex heterogeneous datasets such as those that typically arise in biological studies. This chapter discusses two interesting applications of mixture models in biostatistics, namely, the analysis of cytometry data and of microarray data. We begin with a brief overview of mixture models and a general discussion of recent advances in this area, focusing on trends that are relevant to biostatistics and health science. In particular, we consider techniques that address challenges in analyzing large biomedical datasets, including dimension reduction, the handling of asymmetric and nonnormal clusters, and accounting for inter- and intracluster variations. These are demonstrated via the EMMIX-JCM and EMMIX-contrasts procedures, which are based on random-effects skew mixture models and linear mixed-effects mixture models, respectively. In several applications of EMMIX-JCM to flow cytometric datasets, we illustrate how mixture models can automate the segmentation of cells in samples, align clusters across samples, build batch templates, and predict the labels for new samples. Further illustrations are given using EMMIX-contrasts on real and simulated microarray datasets to showcase the effectiveness of mixture models in clustering gene expression data, ranking genes, and controlling false discovery rate.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofbooktitleDisease Modelling and Public Health, Part A
dc.relation.ispartofchapter4
dc.relation.ispartofpagefrom75
dc.relation.ispartofpageto102
dc.relation.ispartofvolume36
dc.subject.fieldofresearchMedical and Health Sciences not elsewhere classified
dc.subject.fieldofresearchcode119999
dc.titleFinite Mixture Models in Biostatistics
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.hasfulltextNo Full Text
gro.griffith.authorNg, Shu Kay Angus


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