| Title |
Ontology-Based Vaccine and Drug Adverse Event Representation and Theory-Guided Systematic Causal Network Analysis Toward Integrative Pharmacovigilance Research
|
|---|---|
| Published in |
Current Pharmacology Reports, March 2016
|
| DOI | 10.1007/s40495-016-0055-0 |
| Pubmed ID | |
| Authors |
Yongqun He |
| Abstract |
Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 29% |
| Researcher | 5 | 18% |
| Student > Doctoral Student | 3 | 11% |
| Student > Master | 3 | 11% |
| Other | 2 | 7% |
| Other | 0 | 0% |
| Unknown | 7 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 21% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 14% |
| Medicine and Dentistry | 3 | 11% |
| Immunology and Microbiology | 2 | 7% |
| Neuroscience | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 11 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
#15,907,007
of 24,226,848 outputs
Outputs from Current Pharmacology Reports
#72
of 119 outputs
Outputs of similar age
#173,645
of 304,420 outputs
Outputs of similar age from Current Pharmacology Reports
#3
of 8 outputs
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