Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.
Explores deciphering protein interaction fingerprints using geometric deep learning and the challenges in computational protein-protein interaction design.
Explores a unified framework for understanding and evaluating generative sequence models of DNA/RNA or Protein, covering topics like coevolution, conservation, and different models such as GREMLIN and BERT.
Discusses challenges in comparing non-Euclidean data, proposing a Laplacian-based solution for graph alignment and exploring optimal transport for graph distance computation.
Explores predicting protein structure from sequence data and inferring interaction partners through Direct Coupling Analysis and the Iterative Pairing Algorithm.
