Welcome to hegroup.org!
Welcome to the home page of Dr. Yongqun "Oliver" He's laboratory research group at the University of Michigan Medical School, Ann Arbor, MI, USA!
Our group has now focused on computational medicine and bioinformatics research, with primary research interests in the areas of microbiology, vaccinology, nephrology, ontology, literature mining, and machine learning. Our primary scientific research domains are microbiology, vaccinology, and nephrology. Our primary technical research domains are ontology, semantic web, literature mining, and machine learning. These technical domains are all closely related to artificial intelligence (AI), so we do AI research! We are also interested in applying our technologies to other interesting areas as well.
News: Currently we are recruiting a Software Developer / Bioinformatician. Welcome to forward or apply!
A brief introduction of our research is provided below:
(1) Development and applications of community-based ontologies. Ontologies have emerged to become critical to biomedical data and knowledge integration, sharing and validation, as well as new knowledge discovery. A major field of AI is knowledge representation and reasoning (KR², KR&R), for which ontology is a part of the story and critical.
We have initiated and led the development of many community-based ontologies such as the Cell Line Ontology (CLO), Ontology of Biological and Clinical Statistics (OBCS), and Ontology of Host-Microbiome Interactions (OHMI). Dr. He is also an active developer of the Ontology for Biomedical Investigations (OBI), which is collaboratively developed by over 20 communities, and Dr. He is the representation of the vaccine community.
To support the Kidney Precision Medicine Project (KPMP), We have actively involved in the community-based development of the Kidney Tissue Atlas Ontology (KTAO) and Ontology of Precision Medicine Investigation (OPMI).
To face the challenge of the pandemic COVID-19, Dr. He has co-initiated the development of the community-driven Coronavirus Infectious Disease Ontology (CIDO, see the CIDO paper in Scientifc Data). As a first effort to develop the CIDO, we manually collected from the literature over 100 chemical drugs or biological antibodies against pathological human coronaviruses, mapped as many as possible these drugs to existing ontologies, and analyzed them using ontology and bioinformatics methods. The preprint paper about this anti-coronavirus drug study is availabl by clicking here. We have also applied the Vaccine Ontology (VO) and CIDO to model, represent, and analyze COVID-19 vaccines, and are currently developing a COVID-19 vaccine knowledgebase (Cov19VaxKB). As described below, we applied our own reverse vaccinology and mechine learning methods to predict COVID-19 vaccine antigens. Dr. He has also been selected as a faculty member to serve on the President's and Provost's COVID-19 Faculty Council in the University of Michigan.
These ontologies can be used in many applications, including standardized data and knowledge representation, sharing, integration, and advanced analysis. We can also apply ontology to different informatics methods such as literature mining and machine learning.
As a major social activity, Oliver hosted and co-chaired the 2022 International Conference on Biomedical Ontology (ICBO-2022) in September 2022 at the University of Michigan, Ann Arbor, MI, USA.
(2) Development of Ontoanimal tools to support interoperable ontology development and applications.
We have developed many widely used ontology tools (e.g., Ontobee, Ontofox, Ontodog, and Ontorat), collectively names “Ontoanimal” tools. Each tool has specific functions; together, these tools are able to extract ontology subsets, provide ontology community views, generate and edit ontology terms, query and visualize ontology terms, provide statistics of ontologies, and compare ontologies.
We have also initiated the proposal of the eXtensible ontology development (XOD) principles and methods to support ontology interoperability.
With the XOD and our Ontoanimal tools, ontology development is no longer boring and has become more fun and useful!
(3) Vaccine informatics, host-pathogen interactions, and vaccine/drug design and safety analysis.
We study Vaccine Informatics, a branch of vaccinology that Dr. He and his colleagues have advertised and pioneered as referenced here: doi: 10.1155/2010/218590. We developed VIOLIN, the most comprehensive vaccine database and analysis system. The Vaccine Ontology (VO) has become a community effort for standard representation of vaccines, vaccine components, vaccination, and host responses to vaccines. We have also developed Vaxign and Vaxign2, the first web-based publically available vaccine target design tool based on bioinformatics analysis of genome sequences using the strategy of reverse vaccinology.
We have also developed Vaxign-ML, a supervised machine learning model to support vaccine design. We have also used these tools to predict COVID-19 vaccine candidates.
News: On August 19, 2022, Dr. Oliver He and his collaborators were awarded a 5-year NIH U24 grant entitled
"VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebase" (U24AI171008). Congratulations!
We study host-pathogen interactions. We developed PHIDIAS, the most comprehesenive host-pathogen interaction database focusing on human and animal pathogens. As part of PHIDIAS, we have developed Victors, a comprehensive knowledge base of more than 3,000 virulence factors in >120 human and animal pathogens. We have also developed an Ontology of Host-Pathogen Interactions (OHPI) to logically represent the virulence factors and their interactions with the hosts.
We are interested in vaccine and drug safety study. We have led the development of the community-based Ontology of Adverse Events (OAE), and applied it to study vaccine and drug adverse events.
(4) Systematic ontology and bioinformatics analysis of biological interaction networks.
We hypothesized that ontology supports literature mining and analysis of biological interaction networks. As a result, we developed the Interaction Network Ontology (INO) and applied it to enhance literature mining performance. We are also collaborating with our collaborators, including Drs. Junguk Hur and Arzugan Özgür, to apply ontology for more advanced literature mining.
Bayesian network (BN) can model linear, nonlinear, combinatorial, and stochastic relationships among variables across multiple levels of biological organizations. We have developed new BN algorithms and tools for analysis of gene interaction networks using high throughput gene expression data.
In a 2014 paper (He, 2014) and then a 2016 paper (He, 2016), Dr. He proposed a new “OneNet Theory of Life”. The OneNet theory states that the whole process of a life of an organism is a single complex and dynamic network (called “OneNet”).
Dr. He also proposes OneNet tenets to characterize different aspects of the OneNet life. Ontologies and ontology-based bioinformatics tools, such as those introduced above, can be used to integratively represent and study such OneNet theory and OneNet knowledge of different organisms.
(5) Wet-lab (not active now): Host-Brucella interaction and protective vaccine immune mechanisms.
Dr. Oliver He has strong backgrounds in wet-lab microbiology and immunology research. In his M.S. wet-lab research (1993-1996), he developed a new ELISA method, applied the method for epidemiological survey of avian reticuloendotheliosis virus (REV, a RNA virus) outbreak in China, and for the first time found its wide existance in North China. His Ph.D. thesis research (advisor: Dr. Gerhardt G. Schurig) focused on vaccine development and immune mechanism against brucellosis, a zoonotic bacterial disease caused by Brucella. After Dr. He joined the University of Michigan in 2005, we continued the Brucella vaccine research in He lab. One major finding out of our wet-lab studies is the identification of a unique caspase-2-mediated proinflammatory cell death mechanism, which was first observed in our laboratory in macrophages infected with Brucella cattle vaccine RB51 and other rough Brucella strains (but not virulent wild type Brucella strains) (References: He et al, 2006, Chen and He, 2009, Chen et al, 2011, Li and He, 2012, Bronner et al, 2013, and Bronner et al, 2015). Our research has also made Brucella a good model system to study caspase-2 related roles and pathways.
More details about our projects can be found here.
Your suggestions, comments, and collaborations are welcome. Thank you!