A major challenge in the vaccine research has been to identify important vaccine-related networks and logically explain the results. In this paper, we showed that networkbased analysis of vaccine-related networks can discover the underlying structure information consistent with that captured by the Vaccine Ontology and propose new hypotheses for vaccine disease or gene associations. First, a vaccine-vaccine network was inferred using a bipartite network projection strategy on the vaccine-disease network extracted from the Semantic MEDLINE database. In total, 76 vaccines and 573 relationships were identified to construct the vaccine network. The shortest paths between all pairs of vaccines were calculated within the vaccine network. The correlation between the shortest paths of vaccine pairs and their semantic similarities in the Vaccine Ontology was then investigated. Second, a vaccinegene network was also constructed, in which several important vaccine-related genes were identified. This study demonstrated that a combinatorial analysis using literature knowledgebase, semantic technology, and ontology is able to reveal unidentified important knowledge critical to biomedical research and public health and generate testable hypotheses for future experimental verification.
Vaccines have been one of the most successful public
health interventions to date with most vaccine-preventable
diseases having declined in the United States by at least
95-99% (1994). However, vaccine development is getting
more difficult as more complex organisms become
vaccine targets. In recent years, drug repositioning has been
growing in last few years and achieved a number of
successes for existing drugs such as Viagra
By definition, drug repositioning is the “process of
finding new users outside the scope of the original medical
indications for existing drugs or compounds”
However, many of these drug repositioning have been
serendipitous discoveries
Such approaches can also be applied in the future vaccine development.
In recent years, high-throughput biological data and
computational systems biology approaches has provided
an unprecedented opportunity to understand the disease
etiology and its underlying cellular subsystems.
Biological knowledge such as drug-disease networks, and
biomedical ontologies have accelerated the development of
network-based approaches to understanding disease
etiology
Such approaches could also be applied in the vaccine
research, aiming to investigate the vaccine-related
associations derived from public knowledgebase such as
PUBMED literature abstracts. For example, a Vaccine
Ontology (VO)-based literature mining research was
reported last year to study all gene interactions associated
with fever alone or both fever and vaccine
Vaccine Adverse Event Reporting System (VAERS)
To analyse the possible common protective immunity or adverse event mechanisms among different vaccines, it is critical to study all possible vaccine-vaccine and vaccine-gene associations using network analysis approaches.
The hypotheses behind this are: (1) if two vaccines have
coupling relationship with common disease(s)/gene(s),
they are linked in the vaccine network; (2) the closer two
vaccines are in the vaccine network, the more similar they
are in the context of literature knowledgebase, such as
Semantic MEDLINE
In this paper, we proposed a network-based approach to investigate the underlying relationships among vaccines in the context of the vaccine-related network derived from Semantic MEDLINE. The distances of the vaccines were further compared with their semantic similarities in the VO. The results demonstrated that the structure information of vaccine network is consistent with that captured by VO. Such network-based analysis can serve as an independent data resource to construct and evaluate biomedical ontologies. In addition, the vaccine-gene network was also constructed based on Semantic MEDLINE information, in which important vaccine-related genes were identified and further investigated by VO and related independent resources.
The rest of the paper is organized as follows. Section 2 introduces the data resources and the proposed networkbased framework. Section 3 illustrates the results generated from each step in the proposed computational framework. Section 4 provides a thorough discussion of the results and concludes the paper.
In this study, we use Semantic MEDLINE as the data
resource to build the networks. Semantic MEDLINE
Our RDF-based Semantic MEDLINE resource currently contains 843k disease-disease, 111k disease-gene, 1277k disease-drug, 248k drug-gene, 1900k drug-drug, and 49k gene-gene associations. Since this resource contains high-level terms (e.g., gene, protein, disease) that are not useful for network analysis, we further manually filtered out these terms using the following strategy. For disease terms, we only included those terms that are included in ICD9. For gene terms, we only include those terms that have an Entrez gene ID. 2.1.2.
We identified those associations relevant to vaccines only. Specifically, vaccine terms were identified based on SNOMED CT (http://www.ihtsdo.org/snomed-ct). All the terms under the SNOMED CT term Vaccine (CUI: C0042210) were first extracted. A manual review by 3 experts further removed those common terms (e.g., bacteria vaccine) or animal vaccine terms. 2.2 2.2.1.
In graph theory, a bipartite network is composed of two non-overlapping sets of nodes and links that connect one node in the first node set with one node in the second node set. The properties of bipartite networks are often investigated by considering the one-mode projection of the bipartite network. The one-mode projection network can be created by connecting two nodes in the same node set if they have at least one common neighboring node in the other node set. For instance, the vaccine-disease association network is one bipartite network: vaccines and diseases constitute two node sets, and links are generated between vaccine and disease if they are associated in the Semantic MEDLINE. Therefore, the vaccine-vaccine network can be investigated by projecting vaccine-disease associations to vaccine-vaccine associations, in which two vaccines are connected if they are associated with at least one same disease. In this work, all links were generated based on the associations extracted from Semantic MEDLINE as described in Section 2.1. A vaccine-vaccine network was generated consisting of all the links identified in vaccine-disease associations. 2.2.2.
The distance between any two vaccines in the vaccine
network was calculated as the length of the shortest path
between them
The vaccine-gene network was constructed by
vaccinegene associations extracted from the drug-gene
associations in our RDF-based data resource. The important
vaccine-related genes were identified by their significant
higher node degree compared to other vaccine/gene in the
same network. The Cytoscape tool
In total, 76 vaccines, annotated by the SNOMED CT term Vaccine (CUI: C0042210), were used to extract related vaccine-disease and vaccine-gene associations from the drug-disease and drug-gene association tables respectively. In the vaccine-disease network, there were 1127 nodes (178 vaccines and 949 diseases) and 1741 vaccine-disease associations. In the vaccine-gene network, there were 170 nodes (85 vaccines and 85 genes) and 94 vaccine-gene associations. One vaccine network was generated by the projection of the vaccine-disease bipartite network, consisting of 76 vaccines and 573 associations. This vaccine network was then used to analyze the vaccine relationships. The derived vaccine-gene network was also investigated by the VO knowledge. 3.2
Fig. 2 showed a heat map of hierarchical analysis results, providing a direct visualization of potential vaccinevaccine associations. Here we selected four relatively big vaccine-vaccine association groups on the diagonal from Fig. 2 and explain them in detail:
1) This group contains 18 very widely-studied
vaccines. Many interesting results are obtained from the
analysis of this group of vaccine-disease-vaccine
associations. For example, the results from this group show that
influenza vaccines and Rabies vaccines have been
associated with the induction of a severe adverse event
GuillainBarré syndrome (GBS)
Our study also identified many other diseases associating with different vaccines. For example, five vaccines (e.g., pertussis vaccine) were found to be associated with various types of antimicrobial susceptibility, and eight vaccines (e.g., influenza vaccine) have been co-studied with patients having the asthma condition. Due to the relative poor annotation of the vaccine data in the Semantic MEDLINE system, the vaccines identified in the semantic analysis were poorly classified. The incorporation of VO in the study clearly classifies these vaccines, leading to better understanding of the result of the Semantic MEDLINE analysis.
2) This group of vaccines, including Q fever vaccine, Parvovirus vaccine, and Tick-borne encephalitis vaccine, is associated with the common disease “Delayed Hypersensitivity”. Delayed type reactions may occur at days after vaccination and often raise serious safety concerns. Delayed hypersensitivity is not antibody-mediated but rather is a type of cell-mediated response. The study of common vaccines and related gene and pathway features related to the delayed reaction will help to reveal the cause of DTR and eventually prevent it. While these vaccines are developed against different bacterial or viral diseases, there may be similarities among these vaccines, such as common vaccine ingredients (e.g., adjuvant) and a shared target to some common biological pathway in humans. An identification of these common features may indicate a common cause of the DTR.
3) This group of vaccines is associated with the
common disease “Mumps”. The vaccines in this group include
Mumps Vaccine, “measles, mumps, rubella, varicella
vaccine”, and “diphtheria-tetanus-pertussis-haemophilus b
conjugate vaccine” (DTP-Hib). The first two vaccines
protect against Mumps. DTP-Hib was compared with a
Mumps vaccine in a study
4) This vaccine group consists of seven vaccines (e.g.,
Brucella abortus vaccine and bovine rhinotracheitis
vaccine) with direct associations between them. They are all
associated with the common term “calve” in the literature
abstracts. Since “calve disease” has a synonym
“Scheuermann’s Disease”, these vaccines have all been linked to
“Scheuermann’s Disease”. This is due to the ambiguity of
the Nature Language Processing (NLP) process. This can
be improved by future improvement of the disambiguity
capacity of NLP tools.
In the vaccine-gene network, many genes were found to
interact with different vaccines. For example, our study
identified that CD40LG (CD40 ligand) is closely
associated with five vaccines: Diphtheria toxoid vaccine,
Cholera vaccine, Tetanus vaccine, Chickenpox vaccine, and
inactivated poliovirus vaccine (Fig. 4). CD40LG plays an
important role in antigen presentation and stimulation of
cytotoxic T lymphocytes
In this paper, we proposed a novel network-based
approach to investigate the vaccine relationships in the
context of vaccine network extracted from PubMed literature
abstracts. The investigations of vaccine-vaccine,
vaccinedisease, and vaccine-gene networks demonstrate that such
literature-based associations can be better analyzed using
VO and such a combinatorial analysis is able to reveal the
association patterns and identify new knowledge. The
identified vaccine-vaccine associations based on
vaccinedisease distance analysis are consistent with their VO
categories and often lead to the generation of new
hypotheses. Our studies discovered some novel
vaccinevaccine relationships by discovering a group of vaccines
associated with some common diseases as demonstrated
in the heat map analysis in the Results section. Due to the
incompleteness of such information existing in the
literature abstracts, such vaccine-vaccine associations need
further validation in independent databases or through
future experimental studies. For example, while our
analysis reveals associations between a group of vaccines and
neurological adverse events, it is noted that the evidences
of these associations, although reported by some PubMed
abstracts, are not necessarily commonly acknowledged
Future extensions of this work include: (1) integration of more comprehensive vaccine-disease association databases (e.g., VAERS system) to construct more complete vaccine-related networks; (2) generation of vaccinerelated gene network by extending the neighbour genes of vaccine-associated genes; (3) network-based investigation of the relationships among vaccines and other drugs using vaccine-drug associations; (4) investigation on possible ways to improve the network by assigning weights or confident rates to different types of associations or associations from different sources.
This project was supported by the National Institute of Health grants 5R01LM009959-02 to HL, and R01AI081062 to YH.