Representation of Protein Dynamics Disentangled by Time-structure-based Prior
Tsuyoshi Ishizone,
Yasuhiro Matsunaga,
Sotaro Fuchigami
et al.
Abstract:Representation learning (RL) is a universal technique for deriving low-dimensional disentangled representations from high-dimensional observations, aiding a multitude of downstream tasks. RL has been extensively applied to various data types, including images and natural language. Here, we analyze molecular dynamics (MD) simulation data of biomolecules in terms of RL to obtain disentangled representations related to their conformational transitions. Currently, state-of-the-art RL techniques, which are mainly m… Show more
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