Explores machine-learned force fields for accurate molecular simulations and the solution of the electronic Schrödinger equation.
Introduces the basics of physics, including mechanics and making predictions based on observations and hypotheses.
Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.
Covers protein contact prediction using Potts models and pseudolikelihood methods, comparing different approaches for contact prediction in proteins.
Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.
Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.
Explores protein-protein interaction networks, binding importance, experimental approaches, drug target identification, and network construction.
Explores proteomics for post-translational modifications and protein interactions.
Delves into predicting protein structure through amino acid contact analysis and advanced computational methods.
Delves into analyzing residue coevolution in protein families to capture native contacts and predict spatial proximity and protein interactions.
