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Results: 299
Number of items: 299
  • Open Access
    Monsonis Centelles, S., Hoefsloot, H. C. J., Khakimov, B., Ebrahimi, P., Lind, M. V., Kristensen, M., de Roo, N., Jacobs, D. M., van Duynhoven, J., Cannet, C., Fang, F., Humpfer, E., Schäfer, H., Spraul, M., Engelsen, S. B., & Smilde, A. K. (2017). Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test. Analytical Chemistry, 89(15), 8004-8012. https://doi.org/10.1021/acs.analchem.7b01329
  • Stroeve, J. H. M., Saccenti, E., Bouwman, J., Dane, A., Strassburg, K., Vervoort, J., Hankemeier, T., Astrup, A., Smilde, A. K., van Ommen, B., & Saris, W. H. M. (2016). Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study. Obesity, 24(2), 379-388. https://doi.org/10.1002/oby.21361
  • Fazelzadeh, P., Hangelbroek, R. W. J., Tieland, M., de Groot, C. P. G. M., Verdijk, L. B., van Loon, L. J. C., Smilde, A. K., Alves, R. D. A. M., Vervoort, J., Müller, M., van Duynhoven, J. P. M., & Boekschoten, M. V. (2016). The Muscle Metabolome Differs between Healthy and Frail Older Adults. Journal of Proteome Research, 15(2), 499-509. https://doi.org/10.1021/acs.jproteome.5b00840
  • Gardlo, A., Smilde, A. K., Hron, K., Hrdá, M., Karlíková, R., Friedecký, D., & Adam, T. (2016). Normalization techniques for PARAFAC modeling of urine metabolomic data. Metabolomics, 12(7), Article 117. https://doi.org/10.1007/s11306-016-1059-9
  • Mitra, V., Govorukhina, N., Zwanenburg, G., Hoefsloot, H., Westra, I., Smilde, A., Reijmers, T., van der Zee, A. G. J., Suits, F., Bischoff, R., & Horvatovich, P. (2016). Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis. Analytical Chemistry, 88(8), 4229-4238. https://doi.org/10.1021/acs.analchem.5b03483
  • Open Access
    Kutzera, J. (2016). Computational interaction proteomics: from proteome to complexome. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van der Kloet, F. M., Sebastián-León, P., Conesa, A., Smilde, A. K., & Westerhuis, J. A. (2016). Separating common from distinctive variation. BMC Bioinformatics, 17(Suppl 5), Article 195. https://doi.org/10.1186/s12859-016-1037-2
  • Reshetova, P., Smilde, A. K., Westerhuis, J. A., & van Kampen, A. H. C. (2015). Using Petri nets for experimental design in a multi-organ elimination pathway. Computers in Biology and Medicine, 63, 19-27. https://doi.org/10.1016/j.compbiomed.2015.05.001
  • Vis, D. J., Westerhuis, J. A., Jacobs, D. M., van Duynhoven, J. P. M., Wopereis, S., van Ommen, B., Hendriks, M. M. W. B., & Smilde, A. K. (2015). Analyzing metabolomics-based challenge test. Metabolomics, 11(1), 50-63. https://doi.org/10.1007/s11306-014-0673-7
  • Westerhuis, J. A., van Velzen, E. J. J., Jansen, J. J., Hoefsloot, H. C. J., & Smilde, A. K. (2015). Analysis of high-dimensional data from designed metabolomics studies. In M. Grootveld (Ed.), Metabolic profiling: disease and xenobiotics (pp. 117-136). (Issues in Toxicology; No. 21). Royal Society of Chemistry. https://doi.org/10.1039/9781849735162
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