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Wouter Boomsma
Researcher at University of Copenhagen
Publications - 62
Citations - 2702
Wouter Boomsma is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Biology & Protein structure prediction. The author has an hindex of 24, co-authored 53 publications receiving 2141 citations. Previous affiliations of Wouter Boomsma include Technical University of Denmark & University of Cambridge.
Papers
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Journal ArticleDOI
Ancient Biomolecules from Deep Ice Cores Reveal a Forested Southern Greenland
Eske Willerslev,Enrico Cappellini,Wouter Boomsma,Rasmus Nielsen,Martin B. Hebsgaard,Tina B. Brand,Michael Hofreiter,Michael Bunce,Michael Bunce,Hendrik N. Poinar,Dorthe Dahl-Jensen,Sigfus J Johnsen,Jørgen Peder Steffensen,Ole Bennike,Jean-Luc Schwenninger,Roger Nathan,Simon J. Armitage,Cees-Jan de Hoog,Vasily Alfimov,Marcus Christl,Juerg Beer,Raimund Muscheler,J. D. Barker,Martin Sharp,Kirsty Penkman,James Haile,Pierre Taberlet,M. Thomas P. Gilbert,Antonella Casoli,Elisa Campani,Matthew J. Collins +30 more
TL;DR: It is shown that DNA and amino acids from buried organisms can be recovered from the basal sections of deep ice cores, enabling reconstructions of past flora and fauna in high-altitude southern Greenland.
Proceedings Article
3D steerable CNNs: learning rotationally equivariant features in volumetric data
TL;DR: The experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.
Journal ArticleDOI
Statistical Assignment of DNA Sequences Using Bayesian Phylogenetics
TL;DR: It is shown on real data that the method outperforms Blast searches as a measure of confidence and can help eliminate 80% of all false assignment based on best Blast hit.
Journal ArticleDOI
Combining Experiments and Simulations Using the Maximum Entropy Principle
TL;DR: This Perspectives article gives a broad introduction to the maximum entropy method, in an attempt to encourage its further adoption and highlights each of these contributions in turn.
Journal ArticleDOI
A generative, probabilistic model of local protein structure.
Wouter Boomsma,Kanti V. Mardia,Charles C. Taylor,Jesper Ferkinghoff-Borg,Anders Krogh,Thomas Hamelryck +5 more
TL;DR: This work presents a fully probabilistic, continuous model of local protein structure in atomic detail that makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence–structure correlations in the native state.