Claudia Skok Gibbs

GLM-Prior is a genomic language model that generates sequence-derived priors for gene regulatory network inference, showing strong performance in mammals but limited success in yeast.

This paper introduces PMF-GRN, a variational inference method for single-cell GRN inference, showing improved accuracy and uncertainty estimates compared to existing methods.

The paper identifies a sustained transcription factor code that defines neuronal diversity in Drosophila and shows how modifying this code can convert cell fates in vivo.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

The paper identifies new therapeutic targets in CRLF2-overexpressing B-ALL by analyzing TF-gene regulatory interactions using network inference and experimental validation.