메뉴 건너뛰기




Volumn 23, Issue 12, 2007, Pages 1511-1518

An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL ERROR; ARTICLE; CONTROLLED STUDY; GENE FUNCTION; GENETIC ANALYSIS; INFORMATION SCIENCE; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBABILITY; STEADY STATE;

EID: 34547840185     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm142     Document Type: Article
Times cited : (78)

References (27)
  • 1
    • 24344481300 scopus 로고    scopus 로고
    • Steady-state probabilities for attractors in probabilistic Boolean networks
    • Brun,M. et al. (2005) Steady-state probabilities for attractors in probabilistic Boolean networks. EURASIP J. Signal Processing, 85, 1993-2013.
    • (2005) EURASIP J. Signal Processing , vol.85 , pp. 1993-2013
    • Brun, M.1
  • 2
    • 0034714499 scopus 로고    scopus 로고
    • Gene expression profiling: Monitoring transcription and translation products using DNA microarrays and proteomics
    • Celis,J.E. et al. (2000) Gene expression profiling: Monitoring transcription and translation products using DNA microarrays and proteomics. FEBS Lett., 480, 2-16.
    • (2000) FEBS Lett , vol.480 , pp. 2-16
    • Celis, J.E.1
  • 3
    • 0038724486 scopus 로고    scopus 로고
    • External control in Markovian genetic regulatory networks
    • Datta,A. et al. (2003) External control in Markovian genetic regulatory networks. Mach. Learn., 52, 169-191.
    • (2003) Mach. Learn , vol.52 , pp. 169-191
    • Datta, A.1
  • 4
    • 2342444140 scopus 로고    scopus 로고
    • External control inMarkovian genetic regulatory networks: The imperfect information case
    • Datta,A. et al. (2004) External control inMarkovian genetic regulatory networks: The imperfect information case. Bioinformatics 20, 924-930.
    • (2004) Bioinformatics , vol.20 , pp. 924-930
    • Datta, A.1
  • 5
    • 0036578654 scopus 로고    scopus 로고
    • Topological and causal structure of the yeast transcriptional regulatory network
    • Guelzim,N. et al. (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet., 31, 60-63.
    • (2002) Nat. Genet , vol.31 , pp. 60-63
    • Guelzim, N.1
  • 6
    • 0032809387 scopus 로고    scopus 로고
    • Gene expression profiling, genetic networks, and cellular states: An integrating concept for tumorigenesis and drug discovery
    • Huang,S. (1999) Gene expression profiling, genetic networks, and cellular states: An integrating concept for tumorigenesis and drug discovery. J. Mol. Med., 77, 469-480.
    • (1999) J. Mol. Med , vol.77 , pp. 469-480
    • Huang, S.1
  • 7
    • 0035072249 scopus 로고    scopus 로고
    • Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer
    • Hughes,T.R. et al. (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat. Biotechnol., 19, 342-347.
    • (2001) Nat. Biotechnol , vol.19 , pp. 342-347
    • Hughes, T.R.1
  • 8
    • 0036207347 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory systems: A literature review
    • Jong,H.D. (2002) Modeling and simulation of genetic regulatory systems: a literature review. J. Comp. Biol., 9, 67-103.
    • (2002) J. Comp. Biol , vol.9 , pp. 67-103
    • Jong, H.D.1
  • 10
    • 32644431906 scopus 로고    scopus 로고
    • Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks
    • Lähdesmäki,H. et al. (2006) Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Processing, 86, 814-834.
    • (2006) Signal Processing , vol.86 , pp. 814-834
    • Lähdesmäki, H.1
  • 11
    • 84984933234 scopus 로고    scopus 로고
    • High density synthetic oligonucleotide arrays
    • Lipshutz,R.J. et al. (1999) High density synthetic oligonucleotide arrays. Nat. Genet., 21, 20-24.
    • (1999) Nat. Genet , vol.21 , pp. 20-24
    • Lipshutz, R.J.1
  • 12
    • 0034659898 scopus 로고    scopus 로고
    • Genomics, gene expression and DNA arrays
    • Lockhart,D.J. and Winzeler,E.A. (2000) Genomics, gene expression and DNA arrays. Nature, 405, 827-836.
    • (2000) Nature , vol.405 , pp. 827-836
    • Lockhart, D.J.1    Winzeler, E.A.2
  • 14
    • 34250836844 scopus 로고    scopus 로고
    • A control model for Markovian genetic regulatory networks
    • Ng,M.K. et al. (2006) A control model for Markovian genetic regulatory networks. Trans. Comput. Syst. Biol., 4070, 36-48.
    • (2006) Trans. Comput. Syst. Biol , vol.4070 , pp. 36-48
    • Ng, M.K.1
  • 15
    • 16344368806 scopus 로고    scopus 로고
    • Intervention in context-sensitive probabilistic Boolean networks
    • Pal,R. et al. (2005) Intervention in context-sensitive probabilistic Boolean networks. Bioinformatics, 21, 1211-1218.
    • (2005) Bioinformatics , vol.21 , pp. 1211-1218
    • Pal, R.1
  • 16
    • 84923618271 scopus 로고
    • Minorization conditions and convergence rates for Markov chain Monte Carlo
    • Rosenthal,J.S. (1995) Minorization conditions and convergence rates for Markov chain Monte Carlo. J. Am. Stat. Assoc., 90, 558-566.
    • (1995) J. Am. Stat. Assoc , vol.90 , pp. 558-566
    • Rosenthal, J.S.1
  • 18
    • 0028806048 scopus 로고
    • Quantitative monitoring of gene expression patterns with a complementary DNA microarray
    • Schena,M. et al. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467-470.
    • (1995) Science , vol.270 , pp. 467-470
    • Schena, M.1
  • 19
    • 0035999984 scopus 로고    scopus 로고
    • Binary analysis and optimization-based normalization of gene expression data
    • Shmulevich,I. and Zhang,W. (2002) Binary analysis and optimization-based normalization of gene expression data. Bioinformatics, 18, 555-565.
    • (2002) Bioinformatics , vol.18 , pp. 555-565
    • Shmulevich, I.1    Zhang, W.2
  • 20
    • 0345983657 scopus 로고    scopus 로고
    • From Boolean to probabilistic Boolean networks as models of genetic regulatory networks
    • Shmulevich,I. et al. (2002a) From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proc. IEEE, 90, 1778-1792.
    • (2002) Proc. IEEE , vol.90 , pp. 1778-1792
    • Shmulevich, I.1
  • 21
    • 0036772705 scopus 로고    scopus 로고
    • Gene perturbation and intervention in probabilistic Boolean networks
    • Shmulevich,I. et al. (2002b) Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics, 18, 1319-1331.
    • (2002) Bioinformatics , vol.18 , pp. 1319-1331
    • Shmulevich, I.1
  • 22
    • 0036184629 scopus 로고    scopus 로고
    • Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks
    • Shmulevich,I. et al. (2002c) Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18, 261-274.
    • (2002) Bioinformatics , vol.18 , pp. 261-274
    • Shmulevich, I.1
  • 23
    • 0346505356 scopus 로고    scopus 로고
    • Steady-state analysis of genetic regulatory networks modeled by probabilistic Boolean networks
    • Shmulevich,I. et al. (2003) Steady-state analysis of genetic regulatory networks modeled by probabilistic Boolean networks. Comp. Funct. Genomics, 4, 601-608.
    • (2003) Comp. Funct. Genomics , vol.4 , pp. 601-608
    • Shmulevich, I.1
  • 24
    • 0002054202 scopus 로고    scopus 로고
    • Modeling the complexity of gene networks: Understanding multigenic and pleiotropic regulation
    • Somogyi,R. and Sniegoski,C. (1996) Modeling the complexity of gene networks: Understanding multigenic and pleiotropic regulation. Complexity, 1, 45-63.
    • (1996) Complexity , vol.1 , pp. 45-63
    • Somogyi, R.1    Sniegoski, C.2
  • 25
    • 0031635563 scopus 로고    scopus 로고
    • Modeling the normal and neoplastic cell cycle with 'realistic Boolean genetic networks': Their application for understanding carcinogenesis and assessing therapeutic strategies
    • Szallasi,Z. and Liang,S. (1998) Modeling the normal and neoplastic cell cycle with 'realistic Boolean genetic networks': Their application for understanding carcinogenesis and assessing therapeutic strategies. Proc. Pac. Symp. Biocomput., 3, 66-76.
    • (1998) Proc. Pac. Symp. Biocomput , vol.3 , pp. 66-76
    • Szallasi, Z.1    Liang, S.2
  • 26
    • 0031630211 scopus 로고    scopus 로고
    • Genomic regulation modeled as a network with basins of attraction
    • Wuensche,A. (1998) Genomic regulation modeled as a network with basins of attraction. Proc. Pac. Symp. Biocomput., 3, 89-102.
    • (1998) Proc. Pac. Symp. Biocomput , vol.3 , pp. 89-102
    • Wuensche, A.1
  • 27
    • 34547825448 scopus 로고    scopus 로고
    • Simulation study in probabilistic Boolean network models for genetic regulatory networks
    • Zhang,S. et al. (2007) Simulation study in probabilistic Boolean network models for genetic regulatory networks. Int. J. Data Min. Bioinformatics, 1, 217-240.
    • (2007) Int. J. Data Min. Bioinformatics , vol.1 , pp. 217-240
    • Zhang, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.