AI Medical Compendium Journal:
eLife

Showing 111 to 120 of 137 articles

Intelligent classification of platelet aggregates by agonist type.

eLife
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological ...

Population coupling predicts the plasticity of stimulus responses in cortical circuits.

eLife
Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticit...

Top-down machine learning approach for high-throughput single-molecule analysis.

eLife
Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large dat...

Deciphering anomalous heterogeneous intracellular transport with neural networks.

eLife
Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single traje...

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

eLife
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.

eLife
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to a...

Local online learning in recurrent networks with random feedback.

eLife
Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsi...

Defining host-pathogen interactions employing an artificial intelligence workflow.

eLife
UNLABELLED: For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentati...

Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning.

eLife
The highly organized spatial arrangement of sensory hair cells in the organ of Corti is essential for inner ear function. Here, we report a new analytical pipeline, based on optical clearing of tissue, for the construction of a single-cell resolution...

'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

eLife
Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been el...