This repository contains the code used to analyze the data presented in the manuscript "Specificity and exon target space of splicing modifying compounds”.

Tool-set to support Bayesian evidence synthesis in R

A Python library for benchmarking uncertainty estimation and quantification methods for Machine Learning models predictions.

Deep survival analysis made easy

Parse a simplified JSONPath-like syntax

Public gallery of BR Open Source projects

tinydenseR is a landmark-based platform for single cell data analysis that identifies differentially abundant cell types and differentially expressed features, including subtle within-cluster state changes. Modeling samples as replicates, tinydenseR enhances analytic efficiency and reproducibility while preserving the richness of single cell data

ChemScreener is an active learning pipeline for small-molecule screening. In each cycle, it trains an ensemble of multi-task Chemprop models, scores an unlabeled library, and selects compounds to acquire based on different acquisition strategies.

Bayesian logistic regression with optional EX/NEX modeling enables flexible borrowing from historical or concurrent data. The safety model supports dose-escalation in adaptive Phase I oncology trials with multiple drugs. See Neuenschwander et al. (2008, 2016) for methodology.

This project demonstrates how to train a model that can generate synthetic brain (T1w) MRI data. The work was presented as a poster at ICAART 2025. Our code trains a Hierarchical Amortized GAN (HAGAN) model for 3D high resolution medical image synthesis of healthy subjects in MNI space.