Dataset Information

Network-based analysis of vaccine-related associations reveals consistent knowledge with the vaccine ontology.

ABSTRACT:

Background

Ontologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted reasoning. However, a major challenge in the VO has been to construct ontologies of vaccine functions, given incomplete vaccine knowledge and inconsistencies in how this knowledge is manually curated.

Results

In this study, we show that network-based analysis of vaccine-related networks can identify underlying structural information consistent with that captured by the VO, and commonalities in the vaccine adverse events for vaccines and for diseases to produce new hypotheses about pathomechanisms involving the vaccine and the disease status. First, a vaccine-vaccine network was inferred by applying a bipartite network projection strategy to 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 VO was then investigated. Second, a vaccine-gene network was also constructed. In this network, 4 genes were identified as hubs interacting with at least 3 vaccines, and 4 vaccines were identified as hubs associated with at least 3 genes. These findings correlate with existing knowledge and provide new hypotheses in the fundamental interaction mechanisms involving vaccines, diseases, and genes.

Conclusions

In this study, we demonstrated that a combinatorial analysis using a literature knowledgebase, semantic technology, and ontology is able to reveal important unidentified knowledge critical to biomedical research and public health and to generate testable hypotheses for future experimental verification. As the associations from Semantic MEDLINE remain incomplete, we expect to extend this work by (1) integrating additional association databases to complement Semantic MEDLINE knowledge, (2) extending the neighbor genes of vaccine-associated genes, and (3) assigning confidence weights to different types of associations or associations from different sources.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC4177205 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Publications

Network-based analysis of vaccine-related associations reveals consistent knowledge with the vaccine ontology.

Zhang Yuji Y Tao Cui C He Yongqun Y Kanjamala Pradip P Liu Hongfang H

Journal of biomedical semantics 20131111 1

<h4>Background</h4>Ontologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted reasoning. However, a major challenge in the VO has been to construct ontologies of vaccine functions, given incomplete vaccine knowledge and inconsistencies in how this knowledge is manually curated.<h4>Results</h4>In this study, we show tha ...[more]

PMID: 24209834

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Project description:Vaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for Brucella, the causative agent of brucellosis in humans and animals.The VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 Brucella vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to Brucella vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving Brucella vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated Brucella vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 Brucella-related papers, VO-SciMiner identified 140 Brucella genes associated with Brucella vaccines. These genes included known protective antigens, virulence factors, and genes closely related to Brucella vaccines. These VO-interacting Brucella genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of Brucella vaccines and genes were identified. The asserted and inferred VO hierarchies provide semantic support for inferring novel knowledge of association of vaccines and genes from the retrieved data. New hypotheses were generated based on this analysis approach.VO-SciMiner can be used to improve the efficiency for PubMed searching in the vaccine domain.

Project description:BACKGROUND: The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs) have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN). METHODS: In this study, we proposed a method to identify CRGs based on Gene Ontology (GO) and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene) from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs' GO and network characteristics. Lastly, we evaluated the performance of the proposed method. RESULTS: We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC) for our method is 65.2%, whereas that for the traditional method is 55.2%. CONCLUSIONS: Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable database for pharmacogenomics research.

Dataset Information

Network-based analysis of vaccine-related associations reveals consistent knowledge with the vaccine ontology.

Background

Results

Conclusions

Publications

Network-based analysis of vaccine-related associations reveals consistent knowledge with the vaccine ontology.

Similar Datasets

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