Oriol Gallego

ExoFILT uses transfer learning to automate exocytic event detection in single-particle tracking data, improving speed and consistency.

ModCRE-NN predicts transcription factor DNA-binding motifs by integrating structural and evolutionary data, showing improved performance under low similarity and uncertainty.

ModCRElib is a package for modeling cis-regulatory elements, offering tools for TF-DNA interaction analysis, binding motif prediction, and regulatory complex modeling.

The paper uses multimodal imaging to study the dynamic structure of the exocyst complex during exocytosis, revealing its conformational changes and tethering mechanism.

The paper introduces PICT to measure protein distances in living cells, showing exocyst subunit and interacting protein spatial configurations.

PyF2F measures fluorophore-to-fluorophore distances from PICT data, validated on a published dataset.

The paper introduces a structure-based learning approach to predict TF binding preferences and model TF regulatory complexes, demonstrating its effectiveness over existing methods in cases of remote homology.