AI Medical Compendium Journal:
eLife

Showing 91 to 100 of 137 articles

Analysis of ultrasonic vocalizations from mice using computer vision and machine learning.

eLife
Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect U...

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

eLife
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...

Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.

eLife
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete seq...

Action detection using a neural network elucidates the genetics of mouse grooming behavior.

eLife
Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endopheno...

A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network.

eLife
Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data...

Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer.

eLife
Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Different mutational processes induce different types of DNA mutations, providing 'mutational signatures'...

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.

eLife
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocess...

Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.

eLife
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniq...

Tackling the challenges of bioimage analysis.

eLife
Using multiple human annotators and ensembles of trained networks can improve the performance of deep-learning methods in research.

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

eLife
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the...