A paper published in Biology Methods and Protocols, finds that it is now possible to distinguish wild from farmed salmon using deep learning, potentially greatly improving strategies for environmental ...

White matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images are imaging features in various neurological diseases and essential markers for clinical impairment and ...

Researchers from Clemson University have developed a deep learning tool to better understand how gene-regulatory network (GRN) interactions impact individual drug response, according to a study ...

Convergent evolution—where distinct species independently evolve similar traits or functions, such as the wings of birds and bats—has long fascinated biologists. Now, a research team led by Prof. Zou ...

LINCOLNSHIRE, Ill.--(BUSINESS WIRE)-- Zebra Technologies Corporation (NASDAQ: ZBRA), a leading digital solution provider enabling businesses to intelligently connect data, assets, and people, today ...

A research team from the University of Illinois Urbana-Champaign’s Beckman Institute for Advanced Science and Technology has developed a deep learning-based medical imaging approach designed to tackle ...

A deep neural network lost the accuracy race against random forest and SVM on a genetic disease benchmark, but its Monte ...

After fine-tuning, the VGG16 convolutional neural network outperformed other deep learning models in 2 of 3 key classification tasks. The VGG16 convolutional neural network (CNN) achieved high ...

An artificial intelligence (AI) deep learning tool that estimates the malignancy risk of lung nodules achieved high cancer detection rates while significantly reducing false-positive results. Results ...

Researchers have developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal regions, ...