Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
Scientific Research Publishing

Schmidt, R.M. (2019) Recurrent Neural Networks (RNNs): A Gentle Introduction and Overview. arXiv:1912.05911.

ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical ...
IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
IEEE

ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding

Abstract: Heterogeneous graph neural networks (HGNs) have attracted more and more attention recently due to their wide applications such as node classification, community detection, and recommendation ...
Frontiers

Multi-view united transformer block of graph attention network based autism spectrum disorder recognition

Autism Spectrum Disorder (ASD) identification poses significant challenges due to its multifaceted and diverse nature, necessitating early discovery for operative involvement. In a recent study, there ...