8 papers
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…
Time-Aware Latent Space Bayesian Optimization
Tuan A. Vu, Julien Martinelli, Harri Lähdesmäki
Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative…
SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data
Mine ÃÄretir, Miika Koskinen, Juha Sinisalo +2
In healthcare, risk assessment of patient outcomes has been based on survival analysis for a long time, i.e. modeling time-to-event associations. However, conventional approaches r…