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Bioinformatics
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Clustering software. Stanford University, CA, USA. http://genome-www.stanford.edu/%7Esherlock/cluster.html
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Clustering Software
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Genomics and Bioinformatics Group. National Cancer Institute/National Institutes of Health, Bethesda, MD, USA. http://discover.nci.nih.gov/
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A Bayesian approach to discriminate between alternative DNA sequence segmentations
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MAC5: Bayesian inference of phylogenetic trees from DNA sequences incorporating gaps
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Evolutionary HMMs: A Bayesian approach to multiple alignment
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Holmes I, Bruno WJ: Evolutionary HMMs: A Bayesian approach to multiple alignment. Bioinformatics (2001) 17:803-820.
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Crowley EM: A Bayesian method for finding regulatory segments in DNA. Biopolymers (2001) 58:165-174.
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A wavelet-based multifractal formalism application to the study of DNA sequences
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0003922190
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John Wiley & Sons Inc, New York, NY, USA
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Duda RO, Hart PE, Stork DG (Eds): Pattern Classification, 2nd Edition. John Wiley & Sons Inc, New York, NY, USA (2001).
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Pattern Classification, 2nd Edition
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Duda, R.O.1
Hart, P.E.2
Stork, D.G.3
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19
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0003396310
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MIT Press, Cambridge, MA, USA
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Baldi P, Brunak S (Eds): Bioinformatics, The Machine Learning Approach, 2nd Edition. MIT Press, Cambridge, MA, USA (2001).
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Bioinformatics, The Machine Learning Approach, 2nd Edition
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Baldi, P.1
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Bayesian statistics in genetics: A guide for the uninitiated
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Shoemaker JS, Painter IS, Weir BS: Bayesian statistics in genetics: A guide for the uninitiated. Trends Genet (1999) 15:354-358. A good overview of the application of Bayesian statistics in genetics.
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Trends Genet
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Shoemaker, J.S.1
Painter, I.S.2
Weir, B.S.3
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21
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0033059370
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Bayesian inference on biopolymer models
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Liu JS, Lawrence CE: Bayesian inference on biopolymer models. Bioinformatics (1999) 15:38-52. Although not directly related to gene expression analysis, this article provides an excellent description of Bayesian inference in bioinformatics.
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Bioinformatics
, vol.15
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Liu, J.S.1
Lawrence, C.E.2
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Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays
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Khan, J.1
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Pohida, T.6
Smith, P.D.7
Jiang, Y.8
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Finding pathogenicity islands and gene transfer events in genome data
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A new method for characterizing replacement rate variation in molecular sequences. Application of the Fourier and wavelet models to Drosophila and mammalian proteins
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Genetics
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Morozov, P.1
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Wavelets in Chemistry
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A gene expression database for the molecular pharmacology of cancer
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Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN: A gene expression database for the molecular pharmacology of cancer. Nature Genet (2000) 24:236-244.
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Nature Genet
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Scherf, U.1
Ross, D.T.2
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Lee, J.K.5
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Kohn, K.W.7
Reinhold, W.C.8
Myers, T.G.9
Andrews, D.T.10
Scudiero, D.A.11
Eisen, M.B.12
Sausville, E.A.13
Pommier, Y.14
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Blower P, Yang C, Fligner M, Verducci J, Yu L, Richman S, Weinstein JN: Pharmacogenomics J (2002) in press.
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Pharmacogenomics J
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Blower, P.1
Yang, C.2
Fligner, M.3
Verducci, J.4
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Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12
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Long AD, Mangalam HJ, Chan BYP, Tolled L, Hatfield GW, Baldi P: Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J Biol Chem (2001) 276:19937-19944. This study is closely related to reference [2••] but with less emphasis on statistical details.
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J Biol Chem
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Long, A.D.1
Mangalam, H.J.2
Chan, B.Y.P.3
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Baldi, P.6
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30
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Bayesian estimation of fold-changes in the analysis of gene expression: The PFOLD algorithm
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Theilhaber J, Bushnell S, Jackson A, Fuchs R: Bayesian estimation of fold-changes in the analysis of gene expression: The PFOLD algorithm. J Comput Biol (2001) 8:585-614. An excellent paper that presents a systematic and detailed description of a new algorithm for estimating fold-changes. It also provides a very interesting overview of various experimental details that must be considered and how they can be incorporated. The development of a detailed noise model is presented, which is by itself a significant contribution.
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J Comput Biol
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Theilhaber, J.1
Bushnell, S.2
Jackson, A.3
Fuchs, R.4
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National Center for Genome Resources, Santa Fe, NM, USA
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Cyber-T. National Center for Genome Resources, Santa Fe, NM, USA.http://genomics.biochem.uci.edu/genex/cybert/
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32
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Global gene expression profiling in Escherichia coli K12. The effects of integration host factor
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Arfin SM, Long AD, Ito ET, Tolleri L, Riehle MM, Paegle ES, Hatfield GW: Global gene expression profiling in Escherichia coli K12. The effects of integration host factor. J Biol Chem (2000) 275:29672-29684.
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J Biol Chem
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Arfin, S.M.1
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Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. J Comput Biol (2000) 7:601-620.
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Mixture modelling of gene expression data from microarray experiments
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Ghosh D, Chinnaiyan AM: Mixture modelling of gene expression data from microarray experiments. Bioinformatics (2002) 18:275-286. An interesting paper in which mixture modeling, with a decision step based on Bayes factors, is used to determine the number of true clusters in the analysis of gene expression data.
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Bioinformatics
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, pp. 275-286
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Ghosh, D.1
Chinnaiyan, A.M.2
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Predicting the clinical status of human breast cancer by using gene expression profiles
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West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA (2001) 98:11462-11467.
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Proc Natl Acad Sci USA
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West, M.1
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Spang, R.6
Zuzan, H.7
Olson J.A., Jr.8
Marks, J.R.9
Nevins, J.R.10
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Dynamic architecture of the yeast cell cycle uncovered by wavelet decomposition of expression microarray data
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Klevecz RR: Dynamic architecture of the yeast cell cycle uncovered by wavelet decomposition of expression microarray data. Funct Integr Genomics (2000) 1:186-192. A good example of application of wavelet transformation to time-series gene expression data.
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Funct Integr Genomics
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Klevecz, R.R.1
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Tuning in the transcriptome: Basins of attraction in the yeast cell cycle
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Cell Prolif
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Klevecz, R.R.1
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