Explores deciphering protein interaction fingerprints using geometric deep learning and the challenges in computational protein-protein interaction design.
Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.
Explores predicting protein structure from sequence data and inferring interaction partners through Direct Coupling Analysis and the Iterative Pairing Algorithm.
Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.
Explores protein structure, properties, and analysis techniques, emphasizing the importance of understanding protein charge and interpreting UV and CD spectra.
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.
