eLife

SqueakPose Studio: An end-to-end platform for pose estimation and real-time edge-AI deployment

This important work introduces an integrated open-source platform for behavioral acquisition and pose estimation that substantially improves the accessibility and speed of real-time animal tracking ...
eLife

Arrayed single-gene perturbations identify drivers of human anterior neural tube closure

This landmark study investigates how patterned human gastruloids can provide insights into neural tube closure. Using a screen, they identified positive and negative regulators and defines the ...
The Lancet

Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study

aTaub Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel bFaculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel ...
ahajournals.org

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model

Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current ...
Frontiers

MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets

Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume ...
PNAS

Programming and training rate-independent chemical reaction networks

To program complex behavior in environments incompatible with electronic controllers such as within bioreactors or engineered cells, we turn to chemical information processors. While chemical ...
Frontiers

Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks

Different types of dynamics and plasticity principles found through natural neural networks have been well-applied on Spiking neural networks (SNNs) because of their biologically-plausible efficient ...