Deep survival analysis made easy

A Python framework for evaluating and comparing multi-objective optimization (MOO) algorithms in molecular design and drug discovery.

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.

Tool-set to support Bayesian evidence synthesis in R

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

Monitoring overall survival in pivotal trials for indolent cancer

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

PISCES is a pipeline for rapid transcript quantitation, genetic fingerprinting, and quality control assessment of RNAseq libraries using Salmon.

An R-repository to implement a double robust version of the WATCH workflow published in Sechidis et al. (2024)

generative text-to-imaging model for predicting cell phenotypic responses to compounds via high content imaging