Literature DB >> 19226667

Clustering of gene expression data and end-point measurements by simulated annealing.

Pierre R Bushel1.   

Abstract

Most clustering techniques do not incorporate phenotypic data. Limited biological interpretation is garnered from the informal process of clustering biological samples and then labeling groups with the phenotypes of the samples. A more formal approach of clustering samples is presented. The method utilizes simulated annealing of the Modk-prototypes objective function. Separate weighting terms are used for microarray, clinical chemistry, and histopathology measurements to control the influence of each data domain on the clustering of the samples. The weights are adapted during the clustering process. A cluster's prototype is representative of the phenotype of the cluster members. Genes are extracted from phenotypic prototypes obtained from the livers of rats exposed to acetaminophen (an analgesic and antipyretic agent) that differed in the extent of centrilobular necrosis. Map kinase signaling and linoleic acid metabolism were significant biological processes influenced by the exposures of acetaminophen that manifested centrilobular necrosis.

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Year:  2009        PMID: 19226667      PMCID: PMC2853885          DOI: 10.1142/s021972000900400x

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  23 in total

1.  Validating clustering for gene expression data.

Authors:  K Y Yeung; D R Haynor; W L Ruzzo
Journal:  Bioinformatics       Date:  2001-04       Impact factor: 6.937

2.  Evaluation and optimization of clustering in gene expression data analysis.

Authors:  A Fazel Famili; Ganming Liu; Ziying Liu
Journal:  Bioinformatics       Date:  2004-02-12       Impact factor: 6.937

3.  Transcriptional profiling of the left and median liver lobes of male f344/n rats following exposure to acetaminophen.

Authors:  Richard D Irwin; Joel S Parker; Edward K Lobenhofer; Leo T Burka; Pamela E Blackshear; Molly K Vallant; Edward H Lebetkin; Diane F Gerken; Gary A Boorman
Journal:  Toxicol Pathol       Date:  2005       Impact factor: 1.902

4.  Gene expression profiling of rat livers reveals indicators of potential adverse effects.

Authors:  Alexandra N Heinloth; Richard D Irwin; Gary A Boorman; Paul Nettesheim; Rickie D Fannin; Stella O Sieber; Michael L Snell; Charles J Tucker; Leping Li; Gregory S Travlos; Gordon Vansant; Pamela E Blackshear; Raymond W Tennant; Michael L Cunningham; Richard S Paules
Journal:  Toxicol Sci       Date:  2004-04-14       Impact factor: 4.849

5.  Cluster analysis and display of genome-wide expression patterns.

Authors:  M B Eisen; P T Spellman; P O Brown; D Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-12-08       Impact factor: 11.205

6.  Role of lipid peroxidation as a mechanism of liver injury after acetaminophen overdose in mice.

Authors:  Tamara R Knight; Marc W Fariss; Anwar Farhood; Hartmut Jaeschke
Journal:  Toxicol Sci       Date:  2003-08-27       Impact factor: 4.849

7.  An unsupervised approach to identify molecular phenotypic components influencing breast cancer features.

Authors:  Florin M Selaru; Jing Yin; Andreea Olaru; Yuriko Mori; Yan Xu; Steven H Epstein; Fumiaki Sato; Elena Deacu; Suna Wang; Anca Sterian; Amy Fulton; John M Abraham; David Shibata; Claudia Baquet; Sanford A Stass; Stephen J Meltzer
Journal:  Cancer Res       Date:  2004-03-01       Impact factor: 12.701

8.  Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes.

Authors:  Pierre R Bushel; Russell D Wolfinger; Greg Gibson
Journal:  BMC Syst Biol       Date:  2007-02-23

9.  Cluster-Rasch models for microarray gene expression data.

Authors:  H Li; F Hong
Journal:  Genome Biol       Date:  2001-07-31       Impact factor: 13.583

10.  Genetic algorithms applied to multi-class clustering for gene expression data.

Authors:  Haiyan Pan; Jun Zhu; Danfu Han
Journal:  Genomics Proteomics Bioinformatics       Date:  2003-11       Impact factor: 7.691

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  2 in total

1.  Clustering of High Throughput Gene Expression Data.

Authors:  Harun Pirim; Burak Ekşioğlu; Andy Perkins; Cetin Yüceer
Journal:  Comput Oper Res       Date:  2012-12       Impact factor: 4.008

2.  Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes.

Authors:  Clarlynda R Williams-DeVane; David M Reif; Elaine Cohen Hubal; Pierre R Bushel; Edward E Hudgens; Jane E Gallagher; Stephen W Edwards
Journal:  BMC Syst Biol       Date:  2013-11-04
  2 in total

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