Train deep convolutional neural networks to predict regulatory activity along very long chromosome-scale DNA sequences Score variants according to their predicted influence on regulatory activity ...

Abstract: The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process. But Convolutional Neural Networks (CNN) have provided an ...

Abstract: Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches ...

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

The field of orofacial medicine increasingly recognizes the temporomandibular joint (TMJ) as a complex anatomical and functional unit whose disorders can ...

In 1989, a computer scientist tackled the messy challenge of reading handwritten zip codes for the US Post Office. This ...

Meta has unveiled Brain2Qwerty v2, an AI system that converts brain activity into text without surgery, bringing assistive ...

The on-chip all-optical supernode could improve multi-chip AI communication with lower latency, higher energy efficiency and ...

AI’s “backbone” increasingly means energy, infrastructure, and matrix math powering massive next-generation computing systems.

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...