George Yuan
University of Western Ontario
12 Papers
32 Citations
George Yuan is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Computer science & PDZ domain. The author has an hindex of 5, co-authored 6 publications.
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Papers
Hypertriglyceridemia: its etiology, effects and treatment
TL;DR: The totality of evidence suggests that elevated triglyceride levels likely contribute independently to increased risk of cardiovascular disease, although there is no consensus about appropriate target levels.
PSD-95 regulates CRFR1 localization, trafficking and β-arrestin2 recruitment
Henry A. Dunn,Harpreet S. Chahal,Fabiana A. Caetano,Kevin D. Holmes,George Yuan,Ruchi Parikh,Bryan Heit,Stephen S. G. Ferguson +7 more
TL;DR: It is demonstrated that endogenous PSD-95 can be co-immunoprecipitated with CRFR1 from cortical brain homogenate, and this interaction appears to be primarily via the PDZ-binding motif, which has implications in the design of new treatment strategies for mental illness.
26
Genetic forms of the cardiometabolic syndrome: what can they tell the clinician?
George Yuan,Robert A. Hegele +1 more
TL;DR: The zebra serves as a model that can help us understand the horse, so that the rare partial lipodystrophies might offer some insight into pathogenesis and treatment of the more prevalent CMS.
6
Correlation embedding learning with dynamic semantic enhanced sampling for knowledge graph completion
TL;DR: A dynamic semantic sampling and correlation embedding completion framework that includes a negative sampling algorithm based on dynamic semantic similarity and a correlated embedding model that can enrich embeddings by learning the sequential and correlated information of entities and relations in the knowledge graph.
5
Structure-adaptive graph neural network with temporal representation and residual connections
TL;DR: A structure adaptive graph neural network with temporal representation and residual connections (TR-SAGNN) for brain network classification and an end-to-end adaptive graph structure learning module based on the product-moment self-attention mechanism which avoids manual threshold selection and obtains a more accurate graph structure.
5