4 papers
Emyx: Fast and efficient all-atom protein generation
Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla +6
Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the…
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori +2
Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not…
Weighted-Sum of Gaussian Process Latent Variable Models
James Odgers, Ruby Sedgwick, Chrysoula Kappatou +2
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Pro…
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
Ruby Sedgwick, John P. Goertz, Molly M. Stevens +2
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a sp…