Events/VDOS 2013

Revision as of 20:30, 14 June 2013 by Taocui (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
International Workshop on

Vaccine and Drug Ontology Studies


Montreal, Qc, Canada July 7, 2013

in conjunction with the

4th International Conference on Biomedical Ontology

Tentative Workshop Program

Workshop Theme and Topics

Drugs and vaccines have contributed to dramatic improvements in public health worldwide. Over the last decade, there have been efforts in the biomedical ontology community that represent various areas associated with drugs including vaccines that extend existing health and clinical terminology systems (e.g., SNOMED, RxNorm, NDF- RT, and MedDRA) and their applications to research and clinical data. This workshop will provide a platform for discussing innovative solutions as well as the challenges in the development and application of biomedical ontologies to representing and analyzing drugs and vaccines, their administration, immune responses induced, adverse events, and similar topics. The workshop will cover two main areas: (i) ontology representation of drugs (including vaccines), and (ii) applications of the ontologies in real world situations – administration, adverse events, etc. Examples of biomedical subject matter in the scope of this workshop: drug components (e.g., drug active ingredients, vaccine antigens, and adjuvants), administration details (e.g., dosage, administration route, and frequency), gene immune responses and pathways, drug-drug or drug-food interactions, and adverse events. Both research and clinical subjects will be covered. We will also focus on computational methods used to study these, for example, literature mining of vaccine/drug-gene interaction networks, meta-analysis of host immune responses, and time event analysis of the pharmacological effects.

Drugs and vaccines have been critical to prevent and treat human and animal diseases. Work in both (drugs and vaccines) areas is closely related - from preclinical research and development to manufacturing, clinical trials, government approval and regulation, and post-licensure usage surveillance and monitoring. In a broader scope, vaccine is a special type of drug. However, there are many differences between the two - for example, in case of vaccines, dose, time, route, and frequency of administration are generally known quite precisely. But this is not always the case in drugs. Since vaccines are often administered to healthy people to prevent disease, attribution of an adverse event following vaccination is less likely to be confounded by signs or symptoms of underlying disease. However, separation of manifestation of disease from manifestation of drug effect is often very challenging. In the U.S.A, vaccines are regulated under different laws by the Center for Biologics (CBER) at FDA, while drugs are regulated under the Food Drug and Cosmetic Act by the Center for Drugs (CDER) at FDA. Safety surveillance for vaccines is for the most part carried out by the Center for Disease Control (CDC) in Atlanta, while for drugs it is carried out by the FDA. Due to these similarities and differences between vaccines and chemical drugs, a closer communication between these two areas is important to create effective ontological frameworks around which we can build comparative and predictive systems for both vaccines and drugs.

Although several related ontologies have been initiated with much progress made in the recent years, we still face many challenges in order to fully and logically represent drugs and vaccines, and efficiently use the ontologies. In the case of ontology representation, no consensuses have been achieved on how to ontologically represent many relevant areas, for example: (i) administration dose, route, and frequency, (ii) how to accurately represent adverse events, (iii) drug-drug interactions, drug-food interactions, etc (iv) experimental testing and analysis of vaccine/drug-induced immune responses, and (v) the complexity of time constraints for clinical events post vaccination or medication. Meanwhile, it is also a challenge to efficiently apply biomedical ontologies to solve research and clinical problems. For example, is there any advantage in applying ontologies for advanced literature mining in order to discover gene interaction networks underlying protective immunity or adverse events? How to apply ontologies for personalized medicine? How to use ontologies to improve the performance of complex vaccine/drug research and clinical data analysis? This workshop aims to bring together a diverse group of individuals from clinical, research and pharma-biotech areas to identify, propose, and discuss solutions for important research problems in the ontological representation of vaccine and drug information covering development and preparation, administration, mechanisms of action including induced host immune responses, adverse events, etc. This workshop is expected to support the deeper understanding of vaccine and drug mechanisms and effects. More specific topics will be selected based on attendees’ submissions and interests.

Accepted Papers

  • Qian Zhu, Guoqian Jiang, Liwei Wang and Christopher Chute, "Standardized Drug and Pharmacological Class Network Construction"

Dozens of drug terminologies and resources capture the drug and/or drug class information, ranging from their coverage and adequacy of representation. No transformative ways are available to link them together in a standard way, which hinders data integration and data representation for drug-related clinical and translational studies. In this paper, we introduce our preliminary work for building a standardized drug and drug class network that integrates multiple drug terminological resources, using Anatomical Therapeutic Chemical (ATC) and National Drug File Reference Terminology (NDF-RT) as network backbone, and expanding with RxNorm and Structured Product Label (SPL). In total, the network consists of 39,728 drugs and drug classes. Meanwhile, we calculated and compared structure similarity for each drug / drug class pair from ATC and NDF-RT, and analyzed constructed drug class network from chemical structure perspective.

  • Charalampos Doulaverakis, George Nikolaidis, Athanasios Kleontas and Ioannis Kompatsiaris, "Panacea, a Semantic-enabled Drug Recommendations Discovery Framework"

The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards, such as the ICD-10 and ATC classifications, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on the lightweight SKOS ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. The paper compares the current approach to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modelling and approach to the problem, while also pinpointing the advantages of Panacea.

  • Yu Lin and Yongqun He, "Ontology modeling of genetic susceptibility to adverse events following vaccination"

Administration of different vaccines triggers a variety of adverse events in certain groups of people but not in others. The results may be due to the variation of genetic factors that affects the susceptibility to vaccine adverse events. In this study, we introduce the development of an Ontology of Genetic Susceptibility Factors (OGSF) that is aligned with the Basic Formal Ontology (BFO). OGSF represents the genetic susceptibility, genetic susceptibility factors and adverse events following vaccination using formal ontologies. Two case studies were used to test and validate the model. A SPARQL query, visualization of extracted data as a network and the social network analysis performed on the network, further gave insights on the evaluation and application of the ontology.

  • Zhe He, Christopher Ochs, Larisa Soldatova, Yehoshua Perl, Sivaram Arabandi and James Geller, "Auditing Redundant Import in Reuse of a Top Level Ontology for the Drug Discovery Investigations Ontology"

The use of a top-level ontology, e.g. the Basic Formal Ontology (BFO), as a template for a domain ontology is considered a best practice. This saves design efforts and supports multi-disciplinary research. The Drug Discovery Investigations ontology (DDI) for automated drug discovery investigations followed the best practices and imported BFO. However not all BFO classes were used. Quality assurance is an important process in the development of ontologies. One methodology proven to support quality assurance is based on automatic derivation of abstraction networks (ANs) from the original ontologies. An AN of an ontology is a compact secondary network summarizing the ontology. ANs were shown to support the identification of sets of concepts with higher concentrations of errors than control sets. In this paper, an AN is derived for the DDI, based on object properties. The top node of this AN represents a set of 81 classes without any object properties. Nodes of an AN representing many classes tend to indicate modeling errors. Upon reviewing these 81 classes, we discovered that among them are most of the classes imported from BFO, and that most of these classes are irrelevant for DDI. An algorithm for hiding such irrelevant classes from a specified ontology is described. As many as 18 (56%) of the 32 BFO classes represented by the top node of the AN were hidden from DDI by the algorithm. We conclude that ontologies reusing a top-level ontology should employ this AN-based approach.

  • Rainer Winnenburg, Laritza Rodriguez, Fiona Callaghan, Alfred Sorbello, Ana Szarfman and Olivier Bodenreider, "Aligning Pharmacologic Classes Between MeSH and ATC"

Objective: To align pharmacologic classes in ATC and MeSH with lexical and instance-based techniques. Methods: Lexical alignment: we map the names of ATC classes to MeSH through the UMLS, leveraging normalization and additional synonymy. Instance-based alignment: we associate ATC and MeSH classes through the drugs they share, using the Jaccard coefficient to measure class-class similarity. We use a metric to distinguish between equivalence and inclusion mappings. Results: We found 221 lexical mappings, as well as 343 instance-based mappings, with a limited overlap (61). From the 343 instance-based mappings we classify 113 as equivalence mappings and 230 as inclusion mappings. A limited failure analysis is presented. Conclusion: Our instance-based approach to aligning pharmacologic classes has the prospect of effectively supporting the creation of a mapping of pharmacologic classes between ATC and MeSH. This exploratory investigation needs to be evaluated in order to adapt the thresholds for similarity.

  • Yuji Zhang, Cui Tao, Yongqun He, Pradip Kanjamala and Hongfang Liu, "Analysis of Vaccine-related Networks using Semantic MEDLINE and the Vaccine Ontology"

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 network-based 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 vaccine-gene 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.

  • Josh Hanna, Eric Joseph, Mathias Brochhausen and William Hogan, "Building a Drug Ontology based on RxNorm and Other Sources"

We built the Drug Ontology (DrOn) to meet the requirements of our comparative-effectiveness research use case, because existing artifacts had flaws too fundamental and numerous to meet them. However, one of the obstacles we faced when creating the Drug Ontology (DrOn) was the difficulty in reusing drug information from existing sources. The primary external source we have used at this stage in DrOn’s develop-ment is RxNorm, a standard drug terminology curated by the National Library of Medicine (NLM). To build DrOn, we (1) mined data from historical releases of RxNorm and (2) mapped many RxNorm entities to Chemical Entities of Biological Interest (ChEBI) classes, pulling rele-vant information from ChEBI while doing so.

We built DrOn in a modular fashion to facilitate simpler extension and development of the ontology and to allow reasoning and construction to scale. Classes derived from each source are serialized in separate modules. For example, the classes in DrOn that are programmatically derived from RxNorm stored in a separate module and subsumed by classes in a manually built, realist, upper-level module of DrOn with terms such as ‘clinical drug role’, ‘tablet’, ‘capsule’, etc.

  • Roger Hall, Josh Hanna and William Hogan, "Maintaining the Drug Ontology: an Open-source, Structured Product Label API for the JVM"

Our use case for maintenance of the Drug Ontology includes a semi-automated, daily process capable of importing new, relevant information from a variety of linkable resources, using fast and flexible algorithms with full access to all data. Structured Product Labels contain linkable information regarding FDA approved drug products and the drug packages in which they are sold, as well as ingredients and metadata about the drugs. We created an Application Programming Interface for SPLs using Scala, which will run on any implementation of the Java Virtual Machine (JVM) and is freely available through an open-source license for any non-commercial use.

  • Erica Marcos and Yongqun He, "The Ontology of Vaccine Adverse Events (OVAE) and its usage in representing and analyzing vaccine adverse events"

Licensed human vaccines can induce various adverse events in vaccinated patients. Many known vaccine adverse events (VAEs) have been recorded in the package inserts of commercial vaccine products. To better represent and analyse VAEs, we developed the Ontology of Vaccine Adverse Events (OVAE) as an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO). OVAE has been used to represent and classify the adverse events recorded in package insert documents of commercial vaccines licensed by the USA Food and Drug Administration (FDA). OVAE currently includes over 1100 terms, including 87 distinct types of VAEs associated with 63 human vaccines licensed in the USA. Specific VAE occurrence rates associated with different age groups have been recorded in OVAE. SPARQL scripts were developed to query and analyse the OVAE knowledge base data. The top 10 vaccines accompanying with the highest numbers of VAEs and the top 10 VAEs most frequently observed among vaccines were identified and analysed. Different VAE occurrences in different age groups were also analysed. The ontological representation and analysis of the VAE data associated with licensed human vaccines improves the classification and understanding of vaccine-specific VAEs which supports rational VAE prevention and treatment and benefits public health.


For the paper submission, we will allow three submission formats:

  • full research papers (6 pages) format
  • work in progress / late breaking results (2-3 pages), and
  • a statement of interest (one page) for podium presentation.

The paper format will be the same as the format used in ICBO.

Templates from last year are available at: (word) and (latex)

All the papers will be submitted and handled through Easy Chair.

After the full papers are accepted, we will work with the Journal of Biomedical Semantics (JBMS) editors and reviewers to decide which papers will be formally invited for extension to be included in a thematic series in the JBMS journal. All full-length (6 pages) and short-length (2-3 pages) submissions will go through peer reviews by at least two reviewers. The one-page statement-of-interest submissions will be reviewed by the workshop organizers.

Workshop Schedule/Important Dates

  • Individual Workshop Papers Due: April 26, 2013 (EXTENDED)
  • Notification of Acceptance: May, 25 2013
  • Camera Ready: June 15, 2013
  • First Revision due to JBMS: Aug 15, 2013

Previous Workshop

Workshop Organizers

Cui Tao, PhD Department of Health Sciences Research Mayo Clinic College of Medicine

Yongqun “Oliver” He, DVM, PhD Department of Microbiology and Immunology Unit for Laboratory Animal Medicine Center for Computational Medicine and Bioinformatics University of Michigan Medical School

Luca Toldo, PhD Knowledge Management Merck KGaA 250, Frankfurterstrasse 64293 Darmstadt - Germany

Sivaram Arabandi, MD, MS Ontopro LLC Houston, TX