3 papers
cs.LG2026
Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects
Michael Scholkemper, Sach Mukherjee
Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Rec…
cs.LG2025
Large Language Models for Zero-shot Inference of Causal Structures in Biology
Izzy Newsham, Luka Kovačević, Richard Moulange +2
Genes, proteins and other biological entities influence one another via causal molecular networks. Causal relationships in such networks are mediated by complex and diverse mechani…
stat.ML2024
Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Luka Kovačević, Izzy Newsham, Sach Mukherjee +1
Causal structure learning (CSL) refers to the task of learning causal relationships from data. Advances in CSL now allow learning of causal graphs in diverse application domains, w…