AIMC Topic: Single Molecule Imaging

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Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.

Nucleic acids research
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biologica...

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.

Proceedings of the National Academy of Sciences of the United States of America
Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug ...

In-Cell Single-Molecule Analysis of Molecular State and Reaction Kinetics Coupling.

Advances in experimental medicine and biology
Cellular signaling is regulated by the spatiotemporal dynamics and kinetics of molecular behavior. To investigate the mechanisms at the molecular level, fluorescence single-molecule analysis is an effective method owing to the direct observation of i...

Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion.

Physical review. E
Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For...

Large-scale single-molecule imaging aided by artificial intelligence.

Microscopy (Oxford, England)
Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena,...

Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.

Bioinformatics (Oxford, England)
MOTIVATION: Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecula...