Martin Pirkl

Geno2pheno: Interpretable and accurate HIV antibody resistance prediction

The paper develops a web service called geno2pheno[bNAbs] that predicts HIV-1 resistance to broadly neutralizing antibodies using statistical models and compares its performance to other methods.

Identifying cancer pathway dysregulations using differential causal effects

This paper introduces a method to detect dysregulated signaling pathways in cancer by comparing normal and cancerous cells using causal inference, showing improved performance over existing methods.

Inferring perturbation profiles of cancer samples

The paper develops a method to infer patient-specific gene perturbation profiles from cancer data using causal networks and shows its effectiveness in simulations and real cancer samples.

Within-patient genetic diversity of SARS-CoV-2

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.

Single cell network analysis with a mixture of Nested Effects Models

The paper develops M&NEM, a method to identify cellular sub-populations and their causal networks from single-cell data using a mixture of Nested Effects Models.