AI RESEARCH
Applications of deep generative models to DNA reaction kinetics and to cryogenic electron microscopy
arXiv CS.LG
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ArXi:2604.16851v1 Announce Type: new This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with deep learning. It focuses on two areas: DNA reaction kinetics and cryogenic electron microscopy (cryo-EM). In the first part, we present ViDa, a biophysics-informed framework leveraging variational autoencoders (VAEs) and geometric scattering transforms to generate biophysically-plausible embeddings of DNA reaction kinetics simulations.