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Search results for biblio_type:PhD Thesis
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2017
K. E. Holter, B. Kehlet, A. Devor, T. J. Sejnowski, A. M. Dale, S. W. Omholt, O. P. Ottesen, E. A. Nagelhus, K. Mardal and K. H. Pettersen. "Interstitial Solute Transport in 3D Reconstructed Neuropil Occurs by Diffusion Rather than Bulk Flow." Proceedings of the National Academy of Sciences 114, no. 37 (2017): 9894-9899.
2016
S. Safei, C. P. Bradley, V. Suresh, K. Mithraratne, A. Muller, H. Ho, D. Ladd, L. R. Hellevik, S. W. Omholt, J. G. Chase et al. "Roadmap for cardiovascular circulation model." American Journal of Physiology 594 (2016): 6909-6928.
2014
O. Nordbø, P. Lamata, S. Land, S. A. Niederer, J. M. Aronsen, W. E. Louch, I. Sjaastad, H. Martens, A. B. Gjuvsland, K. Tøndel et al. "A computational pipeline for quantification of mouse myocardial stiffness parameters." Computers in Biology and Medicine 53 (2014): 65-75.
2013
K. Tøndel, J. O. Vik, H. Martens, U. G. Indahl, N. P. Smith and S. W. Omholt. "Hierarchical Multivariate Regression-Based Sensitivity Analysis Reveals Complex Parameter Interaction Patterns in Dynamic Models." Chemometrics and Intelligent Laboratory Systems 120 (2013): 25-41.
H. Martens, K. Tøndel, V. Tafintseva, A. Kohler, E. Plathe, J. O. Vik, A. B. Gjuvsland and S. W. Omholt. "PLS-Based Multivariate Metamodeling of Dynamic Systems." In New Perspectives in Partial Least Squares and Related Methods, edited by H. Abdi, W. W. Chin, V. E. Vinzi, G. Russolillo and L. Trinchera, 3-30. Vol. 56. New York: Springer, 2013.
2012
K. Tøndel, U. G. Indahl, A. B. Gjuvsland, S. W. Omholt and H. Martens. "Multi-Way Metamodelling Facilitates Insight Into the Complex Input-Output Maps of Nonlinear Dynamic Models." BMC Systems Biology 6 (2012): 88.
2011
J. O. Vik, A. B. Gjuvsland, L. Li, K. Tøndel, S. A. Niederer, N. P. Smith, P. J. Hunter and S. W. Omholt. "Genotype-Phenotype Map Characteristics of an in Silico Heart Cell." Frontiers in Physiology 2 (2011): 106.
K. Tøndel, U. G. Indahl, A. B. Gjuvsland, J. O. Vik, P. J. Hunter, S. W. Omholt and H. Martens. "Hierarchical Cluster-Based Partial Least Squares Regression Is an Efficient Tool for Metamodelling of Nonlinear Dynamic Models." BMC Systems Biology 5 (2011): 90.