메뉴 건너뛰기




Volumn 1, Issue , 2005, Pages 286-291

Gene regulatory networks inference with recurrent neural network models

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL METHODS; DNA; LEARNING SYSTEMS; RECURRENT NEURAL NETWORKS;

EID: 33745963955     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1555844     Document Type: Conference Paper
Times cited : (5)

References (31)
  • 1
    • 0033031437 scopus 로고    scopus 로고
    • DNA arrays tor analysis of gene expression
    • M. Eisen and P. Brown, "DNA Arrays tor Analysis of Gene Expression," Methods Enzymol, vol. 303, pp. 179-205, 1999.
    • (1999) Methods Enzymol , vol.303 , pp. 179-205
    • Eisen, M.1    Brown, P.2
  • 3
    • 0033736476 scopus 로고    scopus 로고
    • Genetic network inference: From co-expression clustering to reverse engineering
    • P. D'haeseleer, S. Liang, and R. Somogyi, "Genetic Network Inference: From Co-expression Clustering to Reverse Engineering," Bioinformatics, vol. 16, no. 8, pp. 707-726, 2000.
    • (2000) Bioinformatics , vol.16 , Issue.8 , pp. 707-726
    • D'haeseleer, P.1    Liang, S.2    Somogyi, R.3
  • 4
    • 0036207347 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory systems: A literature review
    • H. De Jong, "Modeling and Simulation of Genetic Regulatory Systems: A Literature Review," Journal of Computational Biology, vol. 9, pp. 67-103, 2002.
    • (2002) Journal of Computational Biology , vol.9 , pp. 67-103
    • De Jong, H.1
  • 7
    • 0345983657 scopus 로고    scopus 로고
    • From boolean to probabilistic boolean networks as models of genetic regulatory networks
    • I. Shmulevich, E. Dougherty, and W. Zhang, "From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks", Proceedings of IEEE, vol. 90, no. 11, pp. 1778-1792.
    • Proceedings of IEEE , vol.90 , Issue.11 , pp. 1778-1792
    • Shmulevich, I.1    Dougherty, E.2    Zhang, W.3
  • 10
    • 3242891560 scopus 로고    scopus 로고
    • Estimating gene networks from gene expression data by combing Bayesian network model with promoter element detection
    • Y. Tamada, S. Kim, H.Bannai, S. Imoto, K. Tashiro, S. Kuhara, and S. Miyano, "Estimating Gene Networks from Gene Expression Data by Combing Bayesian Network Model with Promoter Element Detection", Bioinformatics, vol. 19, Suppl.2, pp. ii227-ii236, 2003.
    • (2003) Bioinformatics , vol.19 , Issue.2 SUPPL.
    • Tamada, Y.1    Kim, S.2    Bannai, H.3    Imoto, S.4    Tashiro, K.5    Kuhara, S.6    Miyano, S.7
  • 11
    • 0344464762 scopus 로고    scopus 로고
    • Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
    • D. Husmeier, "Sensitivity and Specificity of Inferring Genetic Regulatory Interactions from Microarray Experiments with Dynamic Bayesian Networks", Bioinformatics, vol. 19, no. 17, pp. 2271-2282, 2003.
    • (2003) Bioinformatics , vol.19 , Issue.17 , pp. 2271-2282
    • Husmeier, D.1
  • 12
    • 0032612220 scopus 로고    scopus 로고
    • Linear modeling of mRNA expression levels during CNS development and injury
    • R.B. Altman, K. Lauderdale, A.K. Dunker, L. Hunter, and T.E. Klein, editors
    • P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, "Linear Modeling of mRNA Expression Levels during CNS Development and Injury", In R.B. Altman, K. Lauderdale, A.K. Dunker, L. Hunter, and T.E. Klein, editors, Proceedings of the Pacific Symposium Biocomputing (PSB'99), pp. 41-52., 1999.
    • (1999) Proceedings of the Pacific Symposium Biocomputing (PSB'99) , pp. 41-52
    • D'haeseleer, P.1    Wen, X.2    Fuhrman, S.3    Somogyi, R.4
  • 17
    • 84898940253 scopus 로고    scopus 로고
    • From co-expression to co-regulation: An approach to inferring transcriptional regulation among gene classes from large-scale expression data
    • MIT Press
    • E. Mjolsness, T. Mann, R. Castaño, and B. Wold, "From Co-expression to Co-regulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-scale Expression Data", in Advances in neural Information Processing Systems 12, pp. 928-934, MIT Press, 2000.
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 928-934
    • Mjolsness, E.1    Mann, T.2    Castaño, R.3    Wold, B.4
  • 19
    • 0035082045 scopus 로고    scopus 로고
    • Neural network model of gene expression
    • J. Vohradsky, "Neural Network Model of Gene Expression", the FASEB Journal, vol. 15, pp. 846-854, 2001.
    • (2001) FASEB Journal , vol.15 , pp. 846-854
    • Vohradsky, J.1
  • 20
    • 0034791625 scopus 로고    scopus 로고
    • Modeling genetic regulatory dynamics in neural development
    • M. Wahde and J. Hertz, "Modeling Genetic Regulatory Dynamics in Neural Development", Journal of Computational Biology. 8, 429-442, 2001.
    • (2001) Journal of Computational Biology , vol.8 , pp. 429-442
    • Wahde, M.1    Hertz, J.2
  • 21
    • 0025503558 scopus 로고
    • Backpropagation through time: What it does and how to do it
    • P.J. Werbos, "Backpropagation Through Time: What It Does And How to Do It", Proceedings of IEEE, 78(10), pp. 1550-1560, 1990.
    • (1990) Proceedings of IEEE , vol.78 , Issue.10 , pp. 1550-1560
    • Werbos, P.J.1
  • 22
    • 0003413187 scopus 로고    scopus 로고
    • Prentice Hall, New Jersey
    • nd Ed., Prentice Hall, New Jersey, 1999.
    • (1999) nd Ed.
    • Haykin, S.1
  • 25
    • 0028338155 scopus 로고
    • An introduction to simulated evolutionary optimization
    • D. Fogel, "An Introduction to Simulated Evolutionary Optimization," IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 3-14, 1994.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.1 , pp. 3-14
    • Fogel, D.1
  • 26
    • 84942134374 scopus 로고    scopus 로고
    • Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks
    • V. Gudise and G. Venayagamoorthy, "Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks", Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 110-117, 2003.
    • (2003) Proceedings of the 2003 IEEE Swarm Intelligence Symposium , pp. 110-117
    • Gudise, V.1    Venayagamoorthy, G.2
  • 27
    • 0034430526 scopus 로고    scopus 로고
    • A particle swarm optimization for reactive power and voltage control considering voltage security assessment
    • H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, "A particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1232-1239, 2000.
    • (2000) IEEE Transactions on Power Systems , vol.15 , Issue.4 , pp. 1232-1239
    • Yoshida, H.1    Kawata, K.2    Fukuyama, Y.3    Takayama, S.4    Nakanishi, Y.5
  • 28
    • 0034104824 scopus 로고    scopus 로고
    • Coarse-grained reverse engineering of genetic regulatory networks
    • M. Wahde and J. Hertz, "Coarse-grained Reverse Engineering of Genetic Regulatory Networks", Biosystems, 55, 129-136, 2000.
    • (2000) Biosystems , vol.55 , pp. 129-136
    • Wahde, M.1    Hertz, J.2
  • 31
    • 0036678794 scopus 로고    scopus 로고
    • Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics
    • M. Ronen, R. Rosenberg, B. Shraiman, and U. Alon, "Assigning Numbers to the Arrows: Parameterizing A Gene Regulation Network by Using Accurate Expression Kinetics", Proc. Natl. Acad. Sci., vol. 99, no. 16, pp. 10555-10560, 2002.
    • (2002) Proc. Natl. Acad. Sci. , vol.99 , Issue.16 , pp. 10555-10560
    • Ronen, M.1    Rosenberg, R.2    Shraiman, B.3    Alon, U.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.