constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts…
we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray
This paper proposes a new bibliometric understanding of persistence that considers the prominent role of collaboration in contemporary science
Combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial and error).
An entity–entity co-occurrence network and employ network indicators analyze the extracted entities.
Develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs virality) on innovation adoption.
Our analysis of 58,728 coronavirus papers show that coronavirus research has become dramatically novel since the outbreak of the COVID-19.
Proposing the edge2vec model, which represents graphs considering edge semantics.
Contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses.
An end-to-end semi-supervised cross-modal contrastive learning framework.