Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily...
我们知道，以COVID-19病毒传播为例，其变异毒株具有传播速度快、潜伏期短、传染性强的特点，这也给疫情防控工作增加了困难。嬴图实时图数据库的图查询分析平台，能够直观地展示出地理位置、相关人员、时间轨迹、相关情况等数据，充分挖掘各个实体之间的传播关系、关联关系，利用“图”的手段呈现出疫情的传播路径，在技...
COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.ResourceThis Knowledge Graphs comprises information encoded in Biological Expression Language (BEL) for a selected corpus around COVID-19. A summary of the corpus is listed here. ...
1783 host genes on 609 pathways, 3635 drugs, 4427 drugs’ targets, and 1285 phenotypes, and their corresponding interactions from a curated list of COVID-19 literature in CTDbase. (b) We built the COVID-19 knowledge graph with nodes (baits, host genes, drugs,targets, pathways,...
最新综述| A Review of Graph Neural Networks in Epidemic Modeling 自COVID-19疫情爆发以来,基于图神经网络(Graph Neural Networks, GNNs)的流行病学建模研究得到了广泛的关注。传统机理模型在数学上描述了传染病的传播机制,但在应对当前复杂多变的流行病学挑战时常显不足。得益于对复杂网络的捕捉能力,GNNs逐渐成为...
COVID-19. Earlier this week,opens in new tabAlicia Frame– the Lead Product Manager for Data Science at Neo4j – was interviewed byopens in new tabKaren Robyat TechRepublic on how Neo4j is being used for drug discovery and other pharmaceutical research in the midst of the coronavirus pandemic...
CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications ArticleOpen access08 July 2022 Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery ...
Since the first coronavirus case was identified in January of 2020, the COVID-19 outbreak seriously threatens the public health of South Korea. Based on the officially notified case data, this paper explored the spatial and temporal evolution of the epidemic in South Korea. Simultaneously, kernel...
ResGNet-C: A graph convolutional neural network for detection of COVID-19一般传统的肺炎检测是通过CT来实现的,本文因此基于CT对肺部所拍摄形成的图,再利用他们所提出的ResGNet-C(C代指covid的缩写)来实现…
without requiring expensive eigenvalue decompositions or matrix inversions. GraphTRSS outperforms several methods for time-varying graph signal reconstruction on two COVID-19 datasets. In addition, our algorithm also performs well on two environmental datasets for the reconstruction of particulate matter ...