Q. Wang
7 Papers
Q. Wang is an academic researcher. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 1, co-authored 7 publications.
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Papers
Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Kaimei Huang,Payal Chandak,Q. Wang,Shreya Havaldar,Akhil Vaid,Jure Leskovec,G. Nadkarni,Benjamin S. Glicksberg,Nils Gehlenborg,Marinka Zitnik +9 more
TL;DR: XGNN as mentioned in this paper is a graph neural network pre-trained on a comprehensive knowledge graph of 17,080 clinically-recognized diseases and 7,957 therapeutic candidates, which can process various therapeutic tasks, such as indication and contraindication prediction, in a unified formulation.
The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities
Eric Venner,Karynne E. Patterson,Divya Kalra,Marsha M. Wheeler,Y.-J. Chen,Salima Kalla,B. Yuan,Jason H. Karnes,Kimberly Walker,J. Smith,Sean McGee,Anjana Radhakrishnan,Anis Haddad,P. Empey,Q. Wang,G. Jarvik,Diana M. Toledo,Anjene Musick,Raphael Gibbs,The All of Us Research Program +19 more
TL;DR: In this article , the authors grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups and found that the European subgroup showed the highest rate of pathogen variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% ('Other').
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Sars-cov-2 antibody levels continue to rise in immune globulin preparations
Q. Wang,H. Park +1 more
TL;DR: In this paper , SARS-CoV-2 antibody levels in commercial intravenous immune globulin (IVIG) products that were prepared between August 2019 to September 2021 and showed a dramatic increase in antibody levels corresponding with increase in COVID-19 cases and vaccine availability.
Analysis of a Large Patient-Level Dataset to Predict Outcome of Treatment for Drug-Resistant Tuberculosis
Q. Wang,Jian Gu,Andrei Gabrielian,Gabriel Rosenfeld,Miguel Quiñones,Darrell E. Hurt,Alex Rosenthal +6 more
TL;DR: This study integrates clinical, radiological, and pathogen genomics into a patient risk model, a way of determining risk through the application of machine learning on real-world data to help establish high-risk patients at the time of admission for tuberculosis treatment.
Sarcopenia, adiposity, and discrepancies in cystatin C versus creatinine-based eGFR in patients with cancer: a cross-sectional study
P. Hanna,Tianzhao Ouyang,I. Tahir,Nurit S. Katz-Agranov,Q. Wang,Lisa Mantz,Ian A. Strohbehn,D. Moreno,D. Harden,J. Dinulos,Didem Cosar,Harish Seethapathy,Justin F. Gainor,S. J. Shah,S. Gupta,David E. Leaf,Florian J. Fintelmann,Meghan E. Sise +17 more
TL;DR: In this article , the relationship between body composition and discrepancies between creatinine and estimated glomerular filtration rate (eGFRCRE) in patients with cancer was evaluated.