Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for ...

Our brains build maps of the environment that help us understand the world around us, allowing us to think, recall, and plan. These maps not only help us to, say, find our room on the correct floor of ...

Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

The GAIN domain plays a central role in the activation of adhesion GPCRs. Until now, research has been limited by the low similarity of the amino acid sequences of different GAIN domains, which has ...

However, domain ... knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to extract knowledge graphs ...

Methods: To address these challenges, we propose PoseGCN, a Graph Convolutional Network (GCN)-based model that integrates ... we introduce an adaptive learning strategy that incorporates ...

GraphPro is a versatile and pluggable OO python library designed for leveraging deep graph learning representations to gain insights into structural proteins and ...

A cornerstone of neural network computation is the concept of weights, which represent the “strength” or “importance” of each neuron’s connection in the network. NPUs integrate these weights directly ...

Abstract: Graph Neural Networks (GNNs) have recently achieved significant success in processing non-Euclidean datasets, such as social and protein-protein interaction networks. However, these datasets ...