Novartis Open Source Initiative

This pipeline is designed for AAV integration site analysis using Target Enrichment Sequencing (TES) coupled with PacBio long-read sequencing. It enables the detection of integration site breakpoints and provides information on the structure of the integrated AAV vector genome to support the safety assessment of AAV-based gene therapies.

Exploratory Graphics for PKPD data

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

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.

Data pre-processing and chemprop model for frequent hitter prediction

Tool-set to support Bayesian evidence synthesis in R

R package for supporting exploratory graphics at http://opensource.nibr.com/xgx

synadam is an R package that generates synthetic ADaM clinical trial datasets from real data using a glimpse-then-simulate approach. It preserves statistical properties and data structure while removing patient identifiers, enabling safe data sharing for pharmaceutical research and regulatory collaboration.

tinydenseR is a landmark-based platform for single cell data analysis that identifies differential cell states and features. Modeling samples as replicates, tinydenseR enhances analytic efficiency and reproducibility while preserving the richness of single cell data.