Super-Resolution of Cryo-EM using ConvNets

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Technische Universität München
Statistical Methods and
Machine Learning for Diffusion MRI
Technische Universität München
Stat. Methods & Machine Learning for Diffusion MRI
• Diffusion MRI:
– Measurement of water diffusion with MRI
– Diffusion in tissue quantifies tissue microstructure & diseases
– High-dimensional data: 3D MRI image × 3D diffusion
• Our work: machine learning streamlines data processing
– Fitting-free: Patient scan time reduced from 25 minutes to 2 minutes!
– Model-free!
– Novelty detection: method doesn’t require prior knowledge about disease
• This project:
– Same methods, new data (Alzheimer’s disease)
– New methods (visualization, dimensionality reduction, metric learning)
– Matlab or Python
Technische Universität München
Super-Resolution of Cryo-EM using
Convolutional Neural Networks
Zika virus
(EMD-8116)
Central Z-slice
Technische Universität München
Super-Resolution of Cryo-EM using ConvNets
• Cryo electron microscopy (cryo-EM)
– Important to solve the structure of macromolecules (proteins),
complexes, organelles, viruses (e.g. Zika virus structure solved
in March)
– Many particles and long acquisition
are not always possible
• This project:
– Improve resolution using deep learning
– Python
Zika virus (EMD-8116)
Z-projection
Technische Universität München
Deep Learning for QSAR
Technische Universität München
Deep Learning for QSAR
• Quantitative structure–activity relationship (QSAR)
– Computer model for relationship between molecule chemical
structure and (biological, pharmaceutical) activity
– Out of millions of small molecules, pre-select promising
candidates for pharmaceutical agents before doing expensive
tests in vitro
– Advanced deep learning methods (e.g. multi-task learning)
achieve better results
• This project:
– Evaluate additional advanced
deep learning methods