AI Medical Compendium Journal:
Biophysical journal

Showing 21 to 30 of 32 articles

Deep learning reduces data requirements and allows real-time measurements in imaging FCS.

Biophysical journal
Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. Firs...

Inferring pointwise diffusion properties of single trajectories with deep learning.

Biophysical journal
To characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so, we propose a machine-learning method to characterize diffusion processes with t...

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Biophysical journal
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA s...

Enhancing robustness, precision, and speed of traction force microscopy with machine learning.

Biophysical journal
Traction patterns of adherent cells provide important information on their interaction with the environment, cell migration, or tissue patterns and morphogenesis. Traction force microscopy is a method aimed at revealing these traction patterns for ad...

A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation.

Biophysical journal
Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechanical stresses...

Active mesh and neural network pipeline for cell aggregate segmentation.

Biophysical journal
Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline comb...

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike.

Biophysical journal
Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel softw...

Deep-learning-based 3D cellular force reconstruction directly from volumetric images.

Biophysical journal
The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a f...

Traction force microscopy by deep learning.

Biophysical journal
Cells interact mechanically with their surroundings by exerting and sensing forces. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiolog...

Neural network strategies for plasma membrane selection in fluorescence microscopy images.

Biophysical journal
In recent years, there has been an explosion of fluorescence microscopy studies of live cells in the literature. The analysis of the images obtained in these studies often requires labor-intensive manual annotation to extract meaningful information. ...