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Results: 297
Number of items: 297
  • Open Access
    Skogholt, J., Liland, K. H., Næs, T., Smilde, A. K., & Indahl, U. G. (2023). Selection of principal variables through a modified Gram–Schmidt process with and without supervision. Journal of Chemometrics, 37(10), Article e3510. https://doi.org/10.1002/cem.3510
  • Open Access
    Großmann, J. L., Westerhuis, J. A., Næs, T., & Smilde, A. K. (2023). Critical evaluation of assessor difference correction approaches in sensory analysis. Food Quality and Preference, 106, Article 104792. https://doi.org/10.1016/j.foodqual.2022.104792
  • Open Access
    Erdős, B., Westerhuis, J. A., Adriaens, M. E., O'Donovan, S. D., Xie, R., Singh-Povel, C. M., Smilde, A. K., & Arts, I. C. W. (2023). Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA. PLoS Computational Biology, 19(6), Article e1011221. https://doi.org/10.1371/journal.pcbi.1011221
  • Open Access
    Nørgaard, S. K., Følsgaard, N., Vissing, N. H., Kyvsgaard, J. N., Chawes, B., Stokholm, J., Smilde, A. K., Bønnelykke, K., Bisgaard, H., & Rasmussen, M. A. (2023). Novel Connections of Common Childhood Illnesses Based on More Than 5 Million Diary Registrations From Birth Until Age 3 Years. Journal of Allergy and Clinical Immunology: In Practice, 11(7), 2162-2171.e6. https://doi.org/10.1016/j.jaip.2023.04.030
  • Smilde, A. K., Næs, T., & Liland, K. H. (2022). Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences. Wiley. https://doi.org/10.1002/9781119600978
  • Open Access
    Li, L., Hoefsloot, H., de Graaf, A. A., Acar, E., & Smilde, A. K. (2022). Exploring dynamic metabolomics data with multiway data analysis: a simulation study. BMC Bioinformatics, 23(1), Article 31. https://doi.org/10.1186/s12859-021-04550-5
  • Open Access
    Khakimov, B., Hoefsloot, H. C. J., Mobaraki, N., Aru, V., Kristensen, M., Lind, M. V., Holm, L., Castro-Mejía, J. L., Nielsen, D. S., Jacobs, D. M., Smilde, A. K., & Engelsen, S. B. (2022). Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance. Analytical Chemistry, 94(2), 628–636. https://doi.org/10.1101/2021.02.24.432509, https://doi.org/10.1021/acs.analchem.1c01654
  • Open Access
    Leygeber, S., Grossmann, J. L., Díez-Simón, C., Karu, N., Dubbelman , A.-C., Harms, A. C., Westerhuis, J. A., Jacobs, D. M., Lindenburg, P. W., Hendriks, M. M. W. B., Ammerlaan, B. C. H., van den Berg, M. A., van Doorn, R., Mumm, R., Hall, R. D., Smilde, A. K., & Hankemeier, T. (2022). Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups. Metabolites, 12(1194), Article 1194. https://doi.org/10.3390/metabo12121194
  • Open Access
    Castro-Mejía, J. L., Khakimov, B., Aru, V., Lind, M. V., Garne, E., Paulová, P., Tavakkoli, E., Hansen, L. H., Smilde, A. K., Holm, L., Engelsen, S. B., & Nielsen, D. S. (2022). Gut Microbiome and Its Cofactors Are Linked to Lipoprotein Distribution Profiles. Microorganisms, 10(11), Article 2156. https://doi.org/10.3390/microorganisms10112156
  • Open Access
    Kim, B., Westerhuis, J. A., Smilde, A. K., Floková, K., Suleiman, A. K. A., Kuramae, E. E., Bouwmeester, H. J., & Zancarini, A. (2022). Effect of strigolactones on recruitment of the rice root-associated microbiome. FEMS Microbiology Ecology, 98(2), Article fiac010. https://doi.org/10.1093/femsec/fiac010
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