메뉴 건너뛰기




Volumn , Issue , 2013, Pages 5-11

Neural network models for classifying immune cell subsets

Author keywords

[No Author keywords available]

Indexed keywords


EID: 84894515658     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2013.6732614     Document Type: Conference Paper
Times cited : (7)

References (30)
  • 1
    • 32944464648 scopus 로고    scopus 로고
    • Pathogen recognition and innate immunity
    • S. Akira, S. Uematsu, and O. Takeuchi. Pathogen recognition and innate immunity. Cell, 124(4):783-801, 2006.
    • (2006) Cell , vol.124 , Issue.4 , pp. 783-801
    • Akira, S.1    Uematsu, S.2    Takeuchi, O.3
  • 6
    • 0035871371 scopus 로고    scopus 로고
    • Artificial neural networks
    • J. E. Dayhoff and J. M. DeLeo. Artificial neural networks. Cancer, 91(S8):1615-1635, 2001.
    • (2001) Cancer , vol.91 , Issue.S8 , pp. 1615-1635
    • Dayhoff, J.E.1    Deleo, J.M.2
  • 9
    • 0032825010 scopus 로고    scopus 로고
    • Mathematical simulation and analysis of cellular metabolism and regulation
    • I. Goryanin, T. C. Hodgman, and E. Selkov. Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics, 15(9):749-758, 1999.
    • (1999) Bioinformatics , vol.15 , Issue.9 , pp. 749-758
    • Goryanin, I.1    Hodgman, T.C.2    Selkov, E.3
  • 10
    • 0030704726 scopus 로고    scopus 로고
    • A cd4+ t-cell subset inhibits antigen-specific t-cell responses and prevents colitis
    • H. Groux, A. O'Garra, M. Bigler, M. Rouleau, S. Antonenko, J. E. de Vries, and M. G. Roncarolo. A cd4+ t-cell subset inhibits antigen-specific t-cell responses and prevents colitis. Nature, 389(6652):737-742, 1997.
    • (1997) Nature , vol.389 , Issue.6652 , pp. 737-742
    • Groux, H.1    O'Garra, A.2    Bigler, M.3    Rouleau, M.4    Antonenko, S.5    De Vries, J.E.6    Roncarolo, M.G.7
  • 11
    • 84871390883 scopus 로고    scopus 로고
    • Neuralnet: Training of neural networks
    • F. Günther and S. Fritsch. neuralnet: Training of neural networks. The R Journal, 2(1):30-38, 2010.
    • (2010) The R Journal , vol.2 , Issue.1 , pp. 30-38
    • Günther, F.1    Fritsch, S.2
  • 12
    • 0028543366 scopus 로고
    • Training feedforward networks with the marquardt algorithm
    • M. T. Hagan and M. B. Menhaj. Training feedforward networks with the marquardt algorithm. Neural Networks, IEEE Transactions on, 5(6):989-993, 1994.
    • (1994) Neural Networks, IEEE Transactions on , vol.5 , Issue.6 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 14
    • 44849096534 scopus 로고    scopus 로고
    • Critical role of il-2 and tgf-β in generation, function and stabilization of foxp3+ cd4+ treg
    • D. A. Horwitz, S. G. Zheng, J. Wang, and J. D. Gray. Critical role of il-2 and tgf-β in generation, function and stabilization of foxp3+ cd4+ treg. European journal of immunology, 38(4):912-915, 2008.
    • (2008) European Journal of Immunology , vol.38 , Issue.4 , pp. 912-915
    • Horwitz, D.A.1    Zheng, S.G.2    Wang, J.3    Gray, J.D.4
  • 17
    • 0037079054 scopus 로고    scopus 로고
    • Computational systems biology
    • H. Kitano. Computational systems biology. Nature, 420(6912):206-210, 2002.
    • (2002) Nature , vol.420 , Issue.6912 , pp. 206-210
    • Kitano, H.1
  • 18
    • 23444439817 scopus 로고    scopus 로고
    • Using process diagrams for the graphical representation of biological networks
    • H. Kitano, A. Funahashi, Y. Matsuoka, and K. Oda. Using process diagrams for the graphical representation of biological networks. Nature biotechnology, 23(8):961-966, 2005.
    • (2005) Nature Biotechnology , vol.23 , Issue.8 , pp. 961-966
    • Kitano, H.1    Funahashi, A.2    Matsuoka, Y.3    Oda, K.4
  • 19
    • 0031568526 scopus 로고    scopus 로고
    • Ifn-gamma-inducing factor (igif) is a costimulatory factor on the activation of th1 but not th2 cells and exerts its effect independently of il-12
    • K. Kohno, J. Kataoka, T. Ohtsuki, Y. Suemoto, I. Okamoto, M. Usui, M. Ikeda, and M. Kurimoto. Ifn-gamma-inducing factor (igif) is a costimulatory factor on the activation of th1 but not th2 cells and exerts its effect independently of il-12. The Journal of Immunology, 158(4):1541-1550, 1997.
    • (1997) The Journal of Immunology , vol.158 , Issue.4 , pp. 1541-1550
    • Kohno, K.1    Kataoka, J.2    Ohtsuki, T.3    Suemoto, Y.4    Okamoto, I.5    Usui, M.6    Ikeda, M.7    Kurimoto, M.8
  • 21
    • 0032165970 scopus 로고    scopus 로고
    • Artificial neural networks for solving ordinary and partial differential equations
    • I. E. Lagaris, A. Likas, and D. I. Fotiadis. Artificial neural networks for solving ordinary and partial differential equations. Neural Networks, IEEE Transactions on, 9(5):987-1000, 1998.
    • (1998) Neural Networks, IEEE Transactions on , vol.9 , Issue.5 , pp. 987-1000
    • Lagaris, I.E.1    Likas, A.2    Fotiadis, D.I.3
  • 22
    • 0344604541 scopus 로고    scopus 로고
    • Artificial neural networks as a tool in ecological modelling, an introduction
    • S. Lek and J.-F. Guégan. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological modelling, 120(2):65-73, 1999.
    • (1999) Ecological Modelling , vol.120 , Issue.2 , pp. 65-73
    • Lek, S.1    Guégan, J.-F.2
  • 27
    • 0029928417 scopus 로고    scopus 로고
    • The expanding universe of t-cell subsets: Th1, th2 and more
    • T. R. Mosmann and S. Sad. The expanding universe of t-cell subsets: Th1, th2 and more. Immunology today, 17(3):138-146, 1996.
    • (1996) Immunology Today , vol.17 , Issue.3 , pp. 138-146
    • Mosmann, T.R.1    Sad, S.2
  • 28
    • 80355131976 scopus 로고    scopus 로고
    • Protective and pathogenic functions of macrophage subsets
    • P. J. Murray and T. A. Wynn. Protective and pathogenic functions of macrophage subsets. NATURE REVIEWS-IMMUNOLOGY, 11:723, 2011.
    • (2011) Nature Reviews-Immunology , vol.11 , pp. 723
    • Murray, P.J.1    Wynn, T.A.2
  • 29
    • 0028148549 scopus 로고
    • Artificial neural networks in the diagnosis and prognosis of prostate cancer: A pilot study
    • P. B. Snow, D. S. Smith, and W. J. Catalona. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. The Journal of urology, 152(5 Pt 2):1923-1926, 1994.
    • (1994) The Journal of Urology , vol.152 , Issue.5 PART 2 , pp. 1923-1926
    • Snow, P.B.1    Smith, D.S.2    Catalona, W.J.3
  • 30
    • 0000243355 scopus 로고
    • Learning in artificial neural networks: A statistical perspective
    • H. White. Learning in artificial neural networks: A statistical perspective. Neural computation, 1(4):425-464, 1989.
    • (1989) Neural Computation , vol.1 , Issue.4 , pp. 425-464
    • White, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.