Yongqun He

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Associate Professor

University of Michigan Medical School

United States

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About

Yongqun He is an Associate Professor at the University of Michigan Medical School in the United States. His research areas encompass vaccine safety, longevity through dietary influences, and precision medicine in kidney health. Recent publications include studies on COVID-19 mRNA vaccines, the impact of climate change on vaccine responses, and the development of ontological models for cancer and parasite vaccines.

Recent Grants

Grant: Close

Ontology-supported Integrative Analysis and Visualization of Vaccine-induced Pathways and Networks

Open Date: 2019-06-12

Close Date: 2021-05-31

Grant: Close

Ontology-based Information Network to Support Vaccine Research

Open Date: 2009-09-22

Close Date: 2014-02-28

Grant: Close

Development and Evaluation of a Dual-Antigen Nasal Brucella Vaccine

Open Date: 2009-07-21

Close Date: 2011-06-30

Grant: Close

GENE EXPRESSION IN BRUCELLA-INFECTED MACROPHAGES

Open Date: 2004-02-15

Close Date: 2008-01-31

Articles (13)

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2025

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2020

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2020

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2020

Collaborators (3)

Christopher Chute

Bloomberg Distinguished Professor of Health Informatics

Johns Hopkins University School of Medicine

UNITED STATES

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Kai Zhang

Empire Innovation Assoc. Prof.

University at Albany, State University of New York

UNITED STATES

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Farrukh M Koraishy

Associate Professor

Stony Brook University

UNITED STATES

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Yongqun He

Research Interests

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About

Recent Grants

Articles (13)

Evaluating the safety of XBB.1.5-containing COVID-19 mRNA vaccines using a self-controlled case series study

Dietary cinnamon promotes longevity and extends healthspan via mTORC1 and autophagy signaling

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Systematic collection, annotation, and pattern analysis of viral vaccines in the VIOLIN vaccine knowledgebase

Impact of climate change on vaccine responses and inequity

Assessing acute kidney injury risk after COVID vaccination and infection in a large cohort study

CancerPro: deciphering the pan-cancer prognostic landscape through combinatorial enrichment analysis and knowledge network insights

Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models

Ontological representation, modeling, and analysis of parasite vaccines

Epitope promiscuity and population coverage of Mycobacterium tuberculosis protein antigens in current subunit vaccines under development

Vaxign-ML: Supervised Machine Learning Reverse Vaccinology Model for Improved Prediction of Bacterial Protective Antigens.

Perform Pathway Enrichment Analysis Using ReactomeFIViz.

Collaborators (3)

Christopher Chute

Kai Zhang

Farrukh M Koraishy

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