Applications of deep learning in electron microscopy
We review the growing use of machine learning in electron microscopy (EM) driven in part
by the availability of fast detectors operating at kiloHertz frame rates leading to large data …
by the availability of fast detectors operating at kiloHertz frame rates leading to large data …
An overview of the recent advances in cryo-electron microscopy for life sciences
Cryo-electron microscopy (CryoEM) has superseded X-ray crystallography and NMR to
emerge as a popular and effective tool for structure determination in recent times. It has …
emerge as a popular and effective tool for structure determination in recent times. It has …
[HTML][HTML] ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation
Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as
Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years …
Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years …
paghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These
fibrous bundles are dense enough to exhibit a certain regularity and mean direction …
fibrous bundles are dense enough to exhibit a certain regularity and mean direction …
Efficient cryo-electron tomogram simulation of macromolecular crowding with application to sars-cov-2
We propose an efficient method for simulating a cryo-Electron Tomography (cryo-ET) image
of a target macromolecule with several neighbor macromolecules packed to achieve a …
of a target macromolecule with several neighbor macromolecules packed to achieve a …
A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation
Background Cryo-electron tomography is an important and powerful technique to explore
the structure, abundance, and location of ultrastructure in a near-native state. It contains …
the structure, abundance, and location of ultrastructure in a near-native state. It contains …
Deep learning-based identification of sub-nuclear structures in FIB-SEM images
Three-dimensional volumetric imaging of cells allows for in situ visualization, thus
preserving contextual insights into cellular processes. Despite recent advances in machine …
preserving contextual insights into cellular processes. Despite recent advances in machine …
Unsupervised multi-task learning for 3D subtomogram image alignment, Clustering and Segmentation
3D subtomogram image alignment, clustering, and segmentation are vital to
macromolecular structure recognition in cryo-electron tomography (cryo-ET). However …
macromolecular structure recognition in cryo-electron tomography (cryo-ET). However …
[HTML][HTML] DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography
Recent biotechnological developments in cryo-electron tomography allow direct
visualization of native sub-cellular structures with unprecedented details and provide …
visualization of native sub-cellular structures with unprecedented details and provide …
Unsupervised Domain Alignment Based Open Set Structural Recognition of Macromolecules Captured By Cryo-Electron Tomography
Cellular cryo-Electron Tomography (cryo-ET) provides three-dimensional views of structural
and spatial information of various macromolecules in cells in a near-native state …
and spatial information of various macromolecules in cells in a near-native state …