Deep and Probabilistic Models for Gene Regulatory Network Inference
Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints. Many methods couple modeling assumptions to a specific inference procedure and rely on heuristic model selection, while evaluation is constrained by incomplete reference networks and point-estimate outputs that lack uncertainty. GRN reconstruction also depends on prior knowledge to constrain TF-gene interactions, yet available priors are often assay-dependent and difficult to transfer across species and less-characterized systems. In this thesis, we develop two complementar...
| Authors | Claudia Skok Gibbs |
|---|---|
| Primary category | stat.ML |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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