4 citations · 14 across the 14 of their papers we have counts for
15 papers · 1 filter
Flexible Flows for Biological Sequence Design
Yogesh Verma, Dani Korpela, Harri Lähdesmäki +1
Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a gene…
In-Context Learning for Latent Space Bayesian Optimization
Tuan A. Vu, Harri Lähdesmäki, Julien Martinelli
Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and prote…
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
Mehmet Yigit Balik, Harri Lähdesmäki
Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fund…
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2
Dynamical modelling is central to many scientific domains, including pharmacometrics, systems biology, physiology, and epidemiology. In these settings, heterogeneity is often intri…
Learning Spatiotemporal Dynamical Systems from Point Process Observations
Valerii Iakovlev, Harri Lähdesmäki
Spatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…