Research output per year
Research output per year
Personal profile
Short presentation
Modern Biology produces data in unprecedented quantities, typically of a high-dimensional nature with non-trivial correlations and substantial levels of noise. Making sense of such data is one of the main challenges of our time, not least in the context of understanding human disease. My research is concerned with developing Machine Learning algorithms in this problem domain.
Currently, my group focuses primarily on problems in molecular biology, in particular in understanding the structure of biomolecules such as proteins. Our work is a mixture of (primarily Bayesian) Statistics, classic Bioinformatics, and Machine Learning.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Dive into the research topics where Wouter Boomsma is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Barnaba: software for analysis of nucleic acid structures and trajectories
Bottaro, S., Bussi, G., Pinamonti, G., Reisser, S., Boomsma, W. & Lindorff-Larsen, K., Feb 2019, In: RNA. 25, 2, p. 219-231Research output: Contribution to journal › Journal article › Research › peer-review
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Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range
Hendus-Altenburger, R., Fernandes, C. B., Bugge, K., Kunze, M. B. A., Boomsma, W. & Kragelund, B. B., 1 Dec 2019, In: Journal of Biomolecular NMR. 73, 12, p. 713–725 13 p.Research output: Contribution to journal › Journal article › Research › peer-review
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The PCNA interaction motifs revisited: thinking outside the PIP-box
Prestel, A., Wichmann, N., Martins, J. M., Marabini, R., Kassem, N., Broendum, S. S., Otterlei, M., Nielsen, O., Willemoës, M., Ploug, M., Boomsma, W. & Kragelund, B. B., 1 Dec 2019, In: Cellular and Molecular Life Sciences. 76, 24, p. 4923-4943 21 p.Research output: Contribution to journal › Journal article › Research › peer-review
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3D steerable CNNs: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W. & Cohen, T., 2018. 12 p.Research output: Contribution to conference › Paper › Research › peer-review
31 Citations (Scopus) -
Driving Structural Transitions in Molecular Simulations Using the Nonequilibrium Candidate Monte Carlo
Kurut, A., Fonseca, R. & Boomsma, W., 25 Jan 2018, In: Journal of Physical Chemistry Part B: Condensed Matter, Materials, Surfaces, Interfaces & Biophysical. 122, 3, p. 1195-1204Research output: Contribution to journal › Journal article › Research › peer-review
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Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias
Wang, Y., Tian, P., Boomsma, W. & Lindorff-Larsen, K., 13 Dec 2018, In: Journal of Physical Chemistry B. 122, 49, p. 11174-11185Research output: Contribution to journal › Journal article › Research › peer-review
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Spherical convolutions and their application in molecular modelling
Boomsma, W. K. & Frellsen, J., 2017, Neural Information Processing Systems 2017. Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R. (eds.). NIPS Proceedings, 11 p. (Advances in Neural Information Processing Systems, Vol. 30).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
26 Citations (Scopus) -
A Monte Carlo study of the early steps of functional amyloid formation
Tian, P., Lindorff-Larsen, K., Boomsma, W. K., Jensen, M. H. & Otzen, D. E., 8 Jan 2016, In: P L o S One. 11, 1, 18 p., e0146096.Research output: Contribution to journal › Journal article › Research › peer-review