AIConfPaper
← Search

Wouter Boomsma

8 accepted papers

2025

Zero-shot protein stability prediction by inverse folding models: a free energy interpretation

Jes Frellsen, Maher M. Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg +1
NeurIPS 2025poster

Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A…

Cited by 0Source ↗code ↗Scholar ↗
2024

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

Miguel González-Duque, Richard Michael, Simon Bartels, Yevgen Zainchkovskyy, Søren Hauberg, Wouter Boomsma
NeurIPS 2024poster

Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these tasks. Several methods for high-dimensional continuous and ca…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2024

BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks

Frederikke Isa Marin, Felix Teufel, Marc Horlacher, Dennis Madsen, Dennis Pultz, Ole Winther +1
ICLR 2024poster

The genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of the various functional, non-coding and regulatory elements encoded in the DNA sequence remains both expensive and c…

Cited by 47Source ↗PDF ↗code ↗Scholar ↗
2024

Kermut: Composite kernel regression for protein variant effects

Peter Mørch Groth, Mads Herbert Kerrn, Lars Olsen, Jesper Salomon, Wouter Boomsma
NeurIPS 2024spotlight

Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

Adaptive Cholesky Gaussian Processes

Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Munoz, Wouter Boomsma, Jes Frellsen, Soren Hauberg
AISTATS 2023poster

We present a method to approximate Gaussian process regression models to large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empirical observation that…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2023

Implicit Variational Inference for High-Dimensional Posteriors

Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma, Jes Frellsen
NeurIPS 2023spotlight

In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional space…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2018

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S Cohen
NeurIPS 2018poster

We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are…

Cited by 618Source ↗PDF ↗code ↗Scholar ↗