메뉴 건너뛰기




Volumn 25, Issue 12, 2009, Pages

Constrained mixture estimation for analysis and robust classification of clinical time series

Author keywords

[No Author keywords available]

Indexed keywords

BETA INTERFERON;

EID: 66349133214     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp222     Document Type: Conference Paper
Times cited : (30)

References (35)
  • 1
    • 0034120945 scopus 로고    scopus 로고
    • The role of b cells and autoantibodies in multiple sclerosis
    • Archelos,J. et al. (2000) The role of b cells and autoantibodies in multiple sclerosis. Ann. Neurol., 47, 694-706.
    • (2000) Ann. Neurol , vol.47 , pp. 694-706
    • Archelos, J.1
  • 3
    • 20044373640 scopus 로고    scopus 로고
    • Transcription-based prediction of response to ifnbeta using supervised computational methods
    • Baranzini,S.E. et al. (2005) Transcription-based prediction of response to ifnbeta using supervised computational methods. PLoS Biol 3, e2.
    • (2005) PLoS Biol , vol.3
    • Baranzini, S.E.1
  • 5
    • 0003857778 scopus 로고    scopus 로고
    • A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
    • Technical Report TR-97-021. International Computer Science Institute, Berkeley
    • Bilmes,J. (1998) A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report TR-97-021. International Computer Science Institute, Berkeley.
    • (1998)
    • Bilmes, J.1
  • 6
    • 36249003318 scopus 로고    scopus 로고
    • Class prediction from time series gene expression profiles using dynamical systems kernel
    • Borgwardt,K.M. et al. (2006) Class prediction from time series gene expression profiles using dynamical systems kernel. Pac. Symp. Biocomput., 11, 547-558.
    • (2006) Pac. Symp. Biocomput , vol.11 , pp. 547-558
    • Borgwardt, K.M.1
  • 7
    • 1642529511 scopus 로고    scopus 로고
    • Metagenes and molecular pattern discovery using matrix factorization
    • Brunet,J.-P. et al. (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA, 101, 4164-4169.
    • (2004) Proc. Natl Acad. Sci. USA , vol.101 , pp. 4164-4169
    • Brunet, J.-P.1
  • 8
    • 0029195475 scopus 로고
    • On the exponential value of labeled samples
    • Castelli,V. and Cover,T.M. (1994) On the exponential value of labeled samples. Patt. Recog. Lett., 16, 105-111.
    • (1994) Patt. Recog. Lett , vol.16 , pp. 105-111
    • Castelli, V.1    Cover, T.M.2
  • 9
    • 33749252873 scopus 로고    scopus 로고
    • Chapelle,O. et al, eds, MIT Press, Cambridge, MA
    • Chapelle,O. et al. (eds) (2006) Semi-supervised Learning. MIT Press, Cambridge, MA.
    • (2006) Semi-supervised Learning
  • 10
    • 42149156031 scopus 로고    scopus 로고
    • Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
    • Costa,I.G. et al. (2007) Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data. BMC Bioinformatics, 8, S3.
    • (2007) BMC Bioinformatics , vol.8
    • Costa, I.G.1
  • 11
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • Eisen,M. et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA, 95, 14863-14868.
    • (1998) Proc. Natl Acad. Sci. USA , vol.95 , pp. 14863-14868
    • Eisen, M.1
  • 12
    • 28644452470 scopus 로고    scopus 로고
    • Clustering short time series gene expression data
    • Ernst,J. et al. (2005) Clustering short time series gene expression data. Bioinformatics, 21, i159-i168.
    • (2005) Bioinformatics , vol.21
    • Ernst, J.1
  • 13
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? which clustering method? answers via model-based cluster analysis
    • Fraley,C. and Raftery,A.E. (1998) How many clusters? which clustering method? answers via model-based cluster analysis. Comput. J., 41, 578-588.
    • (1998) Comput. J , vol.41 , pp. 578-588
    • Fraley, C.1    Raftery, A.E.2
  • 15
    • 18244384210 scopus 로고    scopus 로고
    • Multiple-laboratory comparison of microarray platforms
    • Irizarry,R.A. et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods, 2, 345-350.
    • (2005) Nat. Methods , vol.2 , pp. 345-350
    • Irizarry, R.A.1
  • 16
    • 34248363540 scopus 로고    scopus 로고
    • A patient-gene model for temporal expression profiles in clinical studies
    • Kaminski,N. and Bar-Joseph,Z. (2007) A patient-gene model for temporal expression profiles in clinical studies. J. Computat. Biol., 14 324-338.
    • (2007) J. Computat. Biol , vol.14 , pp. 324-338
    • Kaminski, N.1    Bar-Joseph, Z.2
  • 18
    • 46249094886 scopus 로고    scopus 로고
    • Alignment and classification of time series gene expression in clinical studies
    • Lin,T.H. et al. (2008) Alignment and classification of time series gene expression in clinical studies. Bioinformatics, 24, i147-i155.
    • (2008) Bioinformatics , vol.24
    • Lin, T.H.1
  • 19
    • 84934442362 scopus 로고    scopus 로고
    • Computational diagnostics with gene expression profiles
    • Lottaz,C. et al. (2008) Computational diagnostics with gene expression profiles. Meth. Mol. Biol., 453, 281-296.
    • (2008) Meth. Mol. Biol , vol.453 , pp. 281-296
    • Lottaz, C.1
  • 20
    • 84898984833 scopus 로고    scopus 로고
    • Semi-supervised learning with penalized probabilistic clustering
    • Saul,L.K. et al, eds, MIT Press, Cambridge, MA, USA, pp
    • Lu,Z. and Leen,T. (2005) Semi-supervised learning with penalized probabilistic clustering. In Saul,L.K. et al. (eds), Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA, USA, pp. 849-856.
    • (2005) Advances in Neural Information Processing Systems 17 , pp. 849-856
    • Lu, Z.1    Leen, T.2
  • 22
    • 0038724494 scopus 로고    scopus 로고
    • Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
    • Monti,S. et al. (2003) Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn., 52, 91-118.
    • (2003) Mach. Learn , vol.52 , pp. 91-118
    • Monti, S.1
  • 23
    • 0033010373 scopus 로고    scopus 로고
    • The il-4 receptor: Signaling mechanisms and biologic functions
    • Nelms,K. et al. (1999) The il-4 receptor: Signaling mechanisms and biologic functions. Annu. Rev. Immunol., 17, 701-738.
    • (1999) Annu. Rev. Immunol , vol.17 , pp. 701-738
    • Nelms, K.1
  • 24
    • 0003309997 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • Nigam,K. et al. (1999) Text classification from labeled and unlabeled documents using EM. Mach. Learn., 39, 795-801.
    • (1999) Mach. Learn , vol.39 , pp. 795-801
    • Nigam, K.1
  • 25
    • 34547571030 scopus 로고    scopus 로고
    • g:pRofiler-a web-based toolset for functional profiling of gene lists from large-scale experiments
    • Reimand,J. et al. (2007) g:pRofiler-a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res., 35, W193-W200.
    • (2007) Nucleic Acids Res , vol.35
    • Reimand, J.1
  • 26
    • 0036789860 scopus 로고    scopus 로고
    • Assessment of different treatment failure criteria in a cohort of relapsing-remitting multiple sclerosis patients treated with interferon beta: Implications for clinical trials
    • Ro,J. et al. (2002) Assessment of different treatment failure criteria in a cohort of relapsing-remitting multiple sclerosis patients treated with interferon beta: Implications for clinical trials. Ann. Neurol., 52, 400-406.
    • (2002) Ann. Neurol , vol.52 , pp. 400-406
    • Ro, J.1
  • 27
    • 33646107167 scopus 로고    scopus 로고
    • T cell gene expression profiling identifies distinct subgroups of japanese multiple sclerosis patients
    • Satoh,J.I. et al. (2006) T cell gene expression profiling identifies distinct subgroups of japanese multiple sclerosis patients. J. Neuroimmunol., 174, 108-118.
    • (2006) J. Neuroimmunol , vol.174 , pp. 108-118
    • Satoh, J.I.1
  • 28
    • 4944252468 scopus 로고    scopus 로고
    • Using hidden Markov models to analyze gene expression time course data
    • Schliep,A. et al. (2003) Using hidden Markov models to analyze gene expression time course data. Bioinformatics, 19(Suppl. 1) 255-263.
    • (2003) Bioinformatics , vol.19 , Issue.SUPPL. 1 , pp. 255-263
    • Schliep, A.1
  • 29
    • 19544393245 scopus 로고    scopus 로고
    • Robust inference of groups in gene expression time-courses using mixtures of HMMs
    • Schliep,A. et al. (2004) Robust inference of groups in gene expression time-courses using mixtures of HMMs. Bioinformatics, 20(Suppl. 1), 283-289.
    • (2004) Bioinformatics , vol.20 , Issue.SUPPL. 1 , pp. 283-289
    • Schliep, A.1
  • 32
    • 15944365840 scopus 로고    scopus 로고
    • Diagnostic signatures from microarrays: A bioinformatics concept for personalized medicine
    • Spang,R. (2003) Diagnostic signatures from microarrays: A bioinformatics concept for personalized medicine. BIOSILICO, 1, 64-68.
    • (2003) BIOSILICO , vol.1 , pp. 64-68
    • Spang, R.1
  • 33
    • 33748295732 scopus 로고    scopus 로고
    • A subtype of multiple sclerosis defined by an activated immune defense program
    • van Baarsen,L.G.M. et al. (2006) A subtype of multiple sclerosis defined by an activated immune defense program. Genes Immun., 7 522-531.
    • (2006) Genes Immun , vol.7 , pp. 522-531
    • van Baarsen, L.G.M.1
  • 34
    • 41649114164 scopus 로고    scopus 로고
    • Enabling personalized cancer medicine through analysis of gene-expression patterns
    • van't Veer,L.J. and Bernards,R. (2008) Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature, 452, 564-570.
    • (2008) Nature , vol.452 , pp. 564-570
    • van't Veer, L.J.1    Bernards, R.2
  • 35
    • 21844476447 scopus 로고    scopus 로고
    • Interferon alpha activates nf-kappab in jak1-deficient cells through a tyk2-dependent pathway
    • Yang,C.H. et al. (2005) Interferon alpha activates nf-kappab in jak1-deficient cells through a tyk2-dependent pathway. J. Biol. Chem. 280, 25849-25853.
    • (2005) J. Biol. Chem , vol.280 , pp. 25849-25853
    • Yang, C.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.