title: Identification of sex-associated network patterns in Vaccine-Adverse Event Association Network in VAERS.
abstract: BACKGROUND
  Vaccines are one of the most important public health successes in last century. Besides effectiveness in reducing the morbidity and mortality from many infectious diseases, a successful vaccine program also requires a rigorous assessment on their safety. Due to the limitations of adverse event (AE) data from clinical trials and post-approval surveillance systems, novel computational approaches are needed to organize, visualize, and analyze such high-dimensional complex data.
  
  
  RESULTS
  In this paper, we proposed a network-based approach to investigate the vaccine-AE association network from the Vaccine AE Reporting System (VAERS) data. Statistical summary was calculated using the VAERS raw data and represented in the Resource Description Framework (RDF). The RDF graph was leveraged for network analysis. Specifically, we compared network properties of (1) vaccine - adverse event association network based on reports collected over a 23 year period as well as each year; and (2) sex-specific vaccine-adverse event association network. We observed that (1) network diameter and average path length don't change dramatically over a 23-year period, while the average node degree of these networks changes due to the different number of reports during different periods of time; (2) vaccine - adverse event associations derived from different sexes show sex-associated patterns in sex-specific vaccine-AE association networks.
  
  
  CONCLUSIONS
  We have developed a network-based approach to investigate the vaccine-AE association network from the VAERS data. To our knowledge, this is the first time that a network-based approach was used to identify sex-specific association patterns in a spontaneous reporting system database. Due to unique limitations of such passive surveillance systems, our proposed network-based approaches have the potential to summarize and analyze the associations in passive surveillance systems by (1) identifying nodes of importance, irrespective of whether they are disproportionally reported; (2) providing guidance on sex-specific recommendations in personalized vaccinology.
authors: Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, USA ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.
authors: Yuji
authors: Y
authors: Zhang
authors: University of Wisconsin-Madison, Madison, Winsconsin USA.
authors: Puqiang
authors: P
authors: Wu
authors: Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania USA ; School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas USA.
authors: Yi
authors: Y
authors: Luo
authors: School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas USA.
authors: Cui
authors: C
authors: Tao