Topic: network inference

Develop interpretable computational tools for molecular and spatial analysis of tumour-cell plasticity in pancreatic ductal adenocarcinoma. Within the AIMPACT collaboration, the BioTiC team seeks an engineer to turn prototype differential-network software into a documented Python package, add visualization and example notebooks, and identify regulatory processes for biological follow-up. Relevant skills include software engineering, statistics, machine learning, single-cell analysis, network inference or Python/R; an interest in cancer biology and strong English are valuable. Appointment: 2 ye

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider netwo