Andreas Christ Sølvsten Jørgensen

This paper introduces a Bayesian sparse likelihood-free inference method to reverse-engineer biochemical networks from single-cell data, showing high accuracy and outperforming correlation-based methods.

This paper uses agent-based models and Bayesian inference to forecast the adoption and spread of a marine management initiative in Fiji, identifying key drivers and informing conservation policy.

The paper infers the core gene regulatory network governing seam cell development in C. elegans using smFISH data and computational models.

The paper shows how Rim11 integrates signals from PKA, TORC1, and Ime1 to regulate Ume6 phosphorylation and drive meiotic gene expression in yeast.

The paper develops a method to model capture efficiency in scRNA-seq data to improve inference of transcriptome-wide burst kinetics.

This paper compares machine learning methods to improve inference efficiency for agent-based simulations of real-world phenomena like tumor growth and disease spread.