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
