Wouter Boomsma

Wouter Boomsma

Professor


  1. Published

    Graphical models and directional statistics capture protein structure

    Boomsma, Wouter, Kent, J. T., Mardia, K. V., Taylor, C. C. & Hamelryck, Thomas Wim, 2006, Interdisciplinary Statistics and Bioinformatics. Barber, S., Baxter, P. D., Mardia, K. V. & Walls, R. E. (eds.). Leeds University Press, p. 91-94 4 p.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  2. Published

    IDDomainSpotter: Compositional bias reveals domains in long disordered protein regions—Insights from transcription factors

    Millard, P. S., Bugge, K., Marabini, R., Boomsma, Wouter, Burow, Meike & Kragelund, Birthe Brandt, 2020, In: Protein Science. 29, 1, p. 169-183 15 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Published

    Implicit Variational Inference for High-Dimensional Posteriors

    Uppal, A., Stensbo-Smidt, K., Boomsma, Wouter & Frellsen, J., 2023, Advances in Neural Information Processing Systems 36 pre-proceedings (NeurIPS 2023). NeurIPS Proceedings, 24 p. (Advances in Neural Information Processing Systems, Vol. 36).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  4. Published

    Inference of structure ensembles of flexible biomolecules from sparse, averaged data

    Olsson, S., Frellsen, J., Boomsma, Wouter, Mardia, K. V. & Hamelryck, Thomas Wim, 2013, In: PLoS ONE. 8, 11, 7 p., e79439.

    Research output: Contribution to journalJournal articleResearchpeer-review

  5. Published

    Iterative ratio method: a formal justification for the potentials of mean force

    Valentin, J., Andreetta, C., Paluszewski, M., Borg, M., Frellsen, J., Paulsen, J., Boomsma, Wouter, Bottaro, S., Ferkinghoff-Borg, J. & Hamelryck, Thomas Wim, 2011. 1 p.

    Research output: Contribution to conferencePosterResearch

  6. Published

    Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition

    Bartels, S., Boomsma, Wouter, Frellsen, J. & Garreau, D., 2023, In: Journal of Machine Learning Research. 24, p. 1-57 71.

    Research output: Contribution to journalJournal articleResearchpeer-review

  7. Published

    Learning meaningful representations of protein sequences

    Detlefsen, N. S., Hauberg, S. & Boomsma, Wouter, 2022, In: Nature Communications. 13, 1, p. 1-12 1914.

    Research output: Contribution to journalJournal articleResearchpeer-review

  8. Published

    Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components

    Kampmeyer, Caroline, Grønbæk-Thygesen, Martin, Oelerich, N., Tatham, M. H., Cagiada, Matteo, Lindorff-Larsen, K., Boomsma, Wouter, Hofmann, K. & Hartmann-Petersen, Rasmus, 2023, In: Cellular and Molecular Life Sciences. 80, 6, 18 p., 143.

    Research output: Contribution to journalJournal articleResearchpeer-review

  9. Published

    Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

    Wang, Y., Tian, P., Boomsma, Wouter & Lindorff-Larsen, Kresten, 2018, In: Journal of Physical Chemistry B. 122, 49, p. 11174-11185

    Research output: Contribution to journalJournal articleResearchpeer-review

  10. Multiple sequence alignment using SAGA: investigating the effects of operator scheduling, population seeding, and crossover operators

    Thomsen, R. & Boomsma, Wouter, 2004, Applications of Evolutionary Computing: EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoISAP, EvoMUSART, and EvoSTOC, Coimbra, Portugal, April 5-7, 2004. Proceedings. Raidl, G. R. (ed.). Springer, p. 113-122 10 p. (Lecture notes in computer science, Vol. 3005).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

ID: 40103911