@conference {ICBO_2018_15, title = {ICBO_2018_15: Quality Assurance of Ontology Content Reuse}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Building ontologies is difficult and time-consuming. As such, content reuse has been promoted as an important guiding principle in ontology development. Reusing content from other ontologies can reduce the overall effort involved in new ontology construction and provide better alignment with existing knowledge modeling. However, reuse is not a panacea, and it comes with its own attendant difficulties. In this paper, we investigate some common quality assurance issues associated with reuse, such as duplicated content and versioning problems. Some heuristic-based approaches are proposed for analyzing ontologies for these kinds of quality assurance issues. An analysis is carried out on a sample of the large collection of BioPortal-hosted ontologies, many of which employ reuse. The findings indicate that curators and authors, particularly those new to the reuse process, should be on the alert when developing an ontology with reused content to avoid introducing problems into their own ontologies.

}, keywords = {BioPortal, modeling, Ontology, ontology quality assurance, ontology reuse}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_15.pdf }, author = {Michael Halper and Christopher Ochs and Yehoshua Perl and Sivaram Arabandi and Mark Musen} } @conference {ICBO_2018_2, title = {ICBO_2018_2: Adapting Disease Vocabularies for Curation at the Rat Genome Database}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The Rat Genome Database (RGD) has been annotating genes, QTLs, and strains to disease terms for over 15 years. During that time the controlled vocabulary used for disease curation has changed a few times. The changes were necessitated because no single vocabulary or ontology was freely accessible and complete enough to cover all of the disease states described in the biomedical literature. The first disease vocabulary used at RGD was the {\textquotedblleft}C{\textquotedblright} branch of the National Library of Medicine{\textquoteright}s Medical Subject Headings (MeSH). By 2011 RGD had switched disease curation to the use of MEDIC (MErged DIsease voCabulary), which is a combination of MeSH and OMIM (Online Mendelian Inheritance in Man) constructed by curators at the Comparative Toxicogenomics Database (CTD). MEDIC was an improvement over MeSH, because of the added coverage of OMIM terms, but it was not long before RGD curators saw the need for more disease terms. So within a couple of years, RGD began to add terms to MEDIC under the guise of the RGD Disease Ontology (RDO). Since RGD assigned a unique ID to every MEDIC term imported from CTD, it was easy to add specially coded IDs to indicate those additional terms from a separate, supplemental file. Meanwhile, the human disease ontology (DO) had slowly been developing and expanding. As early as 2010, members of RGD were contributing to the development of DO. Based on the promise of improvements, it was determined that the Alliance of Genome Resources could use the DO as a unifying disease vocabulary across model organism databases. Despite the improvements in DO, RGD still had more than 1000 custom terms and 3800 MEDIC terms with annotations to deal with if RGD would convert to the use of DO. If RGD mapped those non-DO disease terms to DO, much granularity of meaning would be lost. To avoid the loss of granularity it was decided to extend the DO after import of the merged, already axiomized DO file. So after mapping DO completely to the RGD version of MEDIC, a broader, deeper disease vocabulary has been achieved.

}, keywords = {curation, disease vocabularies, online resource, Rat Genome Database}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_2.pdf }, author = {Stan Laulederkind and G. Thomas Hayman and Shur-Jen Wang and Elizabeth Bolton and Jennifer R. Smith and Marek Tutaj and Jeff de Pons and Mary Shimoyama and Melinda Dwinell} } @conference {ICBO_2018_23, title = {ICBO_2018_23: Prot{\'e}g{\'e} 5.5 {\textendash} Improvements for Editing Biomedical Ontologies}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

We present Prot{\'e}g{\'e} 5.5, a significant update to the Prot{\'e}g{\'e} Desktop software, which contains new features that are geared towards editing biomedical ontologies. This version of Prot{\'e}g{\'e} contains user-interface enhancements and optimizations that should make the browsing and editing of OBO-library-style biomedical ontologies easier, faster and more efficient when compared to previous versions of Prot{\'e}g{\'e}.

}, keywords = {Biomedical Ontology Editing, OWL, Prot{\'e}g{\'e}}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_23.pdf }, author = {Matthew Horridge and Rafael S Gon{\c c}alves and Csongor I Nyulas and Tania Tudorache and Mark Musen} } @conference {ICBO_2018_24, title = {ICBO_2018_24: eXtensible ontology development (XOD) using web-based Ontoanimal tools}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The eXtensible ontology development (XOD) strategy proposes four principles to support interoperable and robust ontology development. These principles include ontology term reuse, semantic alignment, design pattern usage, and community extensibility. In this software demo, we show how Ontoanimal tools (e.g., Ontofox, Ontodog, Ontorat, and Ontokiwi) can be used to support the implementation of these XOD principles. The development of the Cell Line Ontology (CLO) is used for the demonstration.

}, keywords = {Cell Line Ontology, CLO, eXtensible ontology development, Ontodog, Ontofox, Ontokiwi, Ontorat, XOD}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_24.pdf }, author = {Edison Ong and Yongqun He and Jie Zheng} } @conference {ICBO_2018_28, title = {ICBO_2018_28: KTAO: A kidney tissue atlas ontology to support community-based kidney knowledge base development and data integration}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Human kidney has its complex structure and diverse interactions among its cells during homeostasis and in its diseased states. To systematically classify, represent, and integrate kidney gene activity, cell types, cell states, and interstitial components, we developed a Kidney Tissue Atlas Ontology (KTAO). KTAO reuses and aligns with existing ontologies such as the Cell Ontology, UBERON, and Human Phenotype Ontology. KTAO also generates new semantic axioms to logically link terms of entities in different domains. As a first study, KTAO represents over 200 known kidney gene markers and their profiles in different cell types in kidney patients. Such a representation supports kidney knowledge base generation, query, and data integration.

}, keywords = {AKI, atlas, CKD, disease, gene marker, Kidney, KTAO, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_28.pdf }, author = {Yongqun He and Becky Steck and Edison Ong and Laura Mariani and Chrysta Lienczewski and Ulysses Balis and Matthias Kretzler and Jonathan Himmelfarb and John F. Bertram and Evren Azeloglu and Ravi Iyengar and Deborah Hoshizaki and Sean D. Mooney} } @conference {ICBO_2018_30, title = {ICBO_2018_30: An Expertise Ontology for Cooperative Extension}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

{The national Cooperative Extension System is a non-formal educational network with a mission of advancing agriculture, the environment, human health and well-being, and community economic development that is coordinated through and distributed across the state land-grant universities. At present there is no easy way to query knowledge assets across individual extension organizations with respect to expertise, accomplished projects, or successful interventions. In collaboration with the umbrella organization eXtension.org, we have developed a prototype ontology for describing expertise across the extension network. This ontology aims to provide a framework enabling linking experts, projects, organizations, competencies, digital resources, and other related assets. There are 14 major classes in this ontology: persons, roles, organizations, competencies, expertise types, subject domains, programs, networks, projects, activities, information resources, audiences, issues, and stakeholders. All these classes are anchored in the Basic Formal Ontology (Arp et al. 2015).Other ontologies used for these classes are FOAF, SKOS, the VIVO Ontology for Researcher Discovery (Mitchell 2018}),and the ASI Sustainable Sourcing Ontology (Hollander 2018). These classes fall into several categories. A couple of these classes such as information resources and subject domains tie into existing taxonomies, for instance subject domains being drawn from the National Institute of Food and Agriculture{\textquoteright}s Manual of Classification for Agricultural and Forestry Research, Education, and Extension (NIFA 2005). Other classes here are intended to support development of databases of instances, for instance directories of persons and organizations with information on subject domain expertise and competencies. Finally, several of these classes occupy structural positions in the ontology, for instance role serving as a class that links persons and organizations. A total of 197 classes are presentlydefined, including those enumerated from various taxonomies. Properties for this ontology have beendrawn from the Relations Ontology (Mungall 2018) and VIVO. At present 14 object properties havebeen incorporated in the ontology.Our development of this expertise ontology is part of a broader initiative to create a set of ontologies describing entities and interactions across the entirety of the food system, ranging from food production, impacts and linkages to the environment, to food consumption, nutrition, and human well-being.

}, keywords = {agriculture, Cooperative Extension, expertise, ontology development}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_30.pdf }, author = {Allan D. Hollander and Christine Geith and Matthew C. Lange} } @conference {ICBO_2018_31, title = {ICBO_2018_31: Test-driven Ontology Development in Prot{\'e}g{\'e}}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Over the past decade, various quality assurance methodologies have emerged in the field of software engineering for preventing, detecting, and fixing faults and bugs in software. In particular, Test-driven Development (TDD) is now a popular quality assurance technique whereby extensive usage of test cases can be used to enforce the correctness of software artifacts. While testing has made its way into the field of ontology engineering, where some techniques for testing ontologies are now used in prominent biomedical ontology projects, Test-driven Development has yet to achieve significant uptake. In this paper we propose a Logic-Based Test-driven Ontology Development methodology, which takes cues from Test-Driven Development in the field of software engineering. Our hope is that this will encourage a "test-first" approach to ontology engineering and that it will form part of the arsenal available to ontology engineers in order to help them produce and maintain high quality ontologies. Test cases in our framework are represented by simple statements describing expected and/or unwanted logical consequences of an intended ontology. As with Test-driven Development in software engineering, our approach encourages small, frequent iterations in the testing and development life-cycle. We provide and present tool support for our approach in the form of OntoDebug {\textendash}- a plug-in for the ontology editor Prot{\'e}g{\'e}.

}, keywords = {ontology quality assurance, Prot{\'e}g{\'e} Plug-In, Test-Driven Ontology Development}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_31.pdf }, author = {Konstantin Schekotihin and Patrick Rodler and Wolfgang Schmid and Matthew Horridge and Tania Tudorache} } @conference {ICBO_2018_38, title = {ICBO_2018_38: Expanding the Molecular Glycophenotype Ontology to include model organisms and acquired diseases}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Glycans are an underappreciated class of molecules despite the fact that they are implicated in more than 100 known diseases. We have developed an ontology model that captures glycan abnormalities at the molecular level (glycophenotypes) called the molecular glycophenotype ontology (MGPO). Only 30\% of known glycosyltransferases have been implicated in human genetic disorders of glycosylation. Ortholog glycosyltransferases from model organism can cover relevant biological information on potential human diseases. Thus, extending MGPO to represent additional phenotypes and support annotation of model organism data will help cross-species comparison. Expansion of MGPO will also include annotation of glycophenotypes from acquired diseases.

}, keywords = {diseases, glycans, glycobiology, glycophenotypes, model organisms., Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_38.pdf }, author = {Jean-Philippe Gourdine and Nicole Vasilevsky and Lilly Winfree and Matthew Brush and Melissa Haendel} } @conference {ICBO_2018_5, title = {ICBO_2018_5: Semantic Integration of Heterogeneous Resources Based on Domain Ontology}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {Heterogeneity has become an important feature of information resources in the network environment. An integrated approach is needed to achieve the disclosure, association, and retrieval of multiple heterogeneous information resources. The traditional integration methods of information resource can solve the heterogeneous problems in terms of system level, syntax level, structure level, etc. The heterogeneity problems at semantic level will be solved under the support of domain ontology with enhanced semantic. This paper applies domain ontology to the semantic integration of heterogeneous information resources, designs a semantic integration model based on domain ontology, and focus on the problems that should be solved during the implementation of this model. This paper discusses the solutions for the problems and applies them to the process of semantic integration of open resources in the field of plant diversity. The application effect is verified to support the service scenes such as semantic retrieval and knowledge browsing in specific subject areas.}, keywords = {domain ontology, open resources, plant diversity, semantic heterogeneity, semantic integration}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Jinjing Guo and Huiling Ren and Jinxia Huang} } @conference {ICBO_2018_56, title = {ICBO_2018_56: Towards the Development of an Opioid Misuse Ontology}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Opioid abuse is a major health crisis in the United States, and it is imperative that patients on an abuse trajectory be identified early. Ontologies, with their semantic representations, provide an advantageous framework for use in early identification of opioid misusers. This paper discusses the early-stage development of the Opioid Misuse Ontology (OMO). Existing ontologies from Ontobee and NCBO Bioportal were reviewed. Data representation for opioid use and misuse was modeled using ontologies with terms from existing resources where possible. Several terms were identified that need to be created. Future directions for OMO include development of new classes, creation of an OWL artifact, publication for public comment, and trialing with electronic medical record data to determine how well it identifies opioid misusers.

}, keywords = {misuse, Ontology, opioids}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_56.pdf }, author = {Corey Hayes and Jonathan Bona and Mathias Brochhausen} } @conference {ICBO_2018_59, title = {ICBO_2018_59: Coordinated Evolution of Ontologies of Informed Consent}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Our project brings together a number of OBO Foundry ontologies related to the representation of informed consent, and expands and enriches them, so that they may provide a representation of informed consent across the informed consent (IC) life cycle. The IC lifecycle involves not only documentation of the consent when originally obtained, but actions that require clear communication of permissions from the initial acquisition of data and specimens through handoffs to, for example, secondary researchers, allowing them access to data or biospecimens referenced in the terms of the original consent. This poster details the progress we have made in representing the domain of consent, as well as the future applications that such work can make possible.

}, keywords = {informed consent, informed consent life cycle, ontology collaboration, reference ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_59.pdf }, author = {J. Neil Otte and Cooper Stansbury and Jonathan Vajda and Frank Manion and Elizabeth Umberfield and Yongqun He and Marcelline Harris and Jihad Obeid and Mathias Brochhausen and William Duncan and Cui Tao} } @conference {ICBO_2018_61, title = {ICBO_2018_61: Using the Oral Health and Disease Ontology to Study Dental Outcomes in National Dental PBRN Practices}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The use of electronic dental records (EDR) has grown rapidly over the past decade, but the development of methods to use EDR data for research and quality improvement is still in its infancy. In this poster, we present our work in which use the Oral Health and Disease Ontology to integrate EDR data from 99 National Dental PBRN practices in order to study the longevity of posterior composite restorations and the rate of tooth loss following root canal treatments.

}, keywords = {dental procedure, dental research, Ontology, posterior composite restoration, root canal treatment}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_61.pdf }, author = {William Duncan and Thankam Thyvalikakath and Zasim Siddiqui and Michelle LaPradd and Chen Wen and Jim Zheng and Anna Roberts and Daniel Hood and Titus Schleyer and Aparna Manimangalam and Donald Rindal and Mark Jurkovich and Tracy Shea and David Bogacz and Terrence Yu and Jeffry Fellows and Valerie Gordan and Gregg Gilbert} } @conference {ICBO_2018_69, title = {ICBO_2018_69: An (ontological) patient perspectiv}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {The Human Phenotype Ontology (HPO) has become the de facto standard representation of clinical {\textquotedblleft}deep phenotype{\textquotedblright} data for computational comparison of abnormalities and for use in genetic disease diagnostics. Using semantic similarity methods, the HPO is used to match non-exact sets of phenotypic features against known diseases, other patients, and model organisms. Algorithms based on HPO have been implemented into variant prioritization tools and are used by the 100,000 Genomes project, the NIH Undiagnosed Diseases Program/Network, and many other clinics, labs, tools, and databases. However, patient phenotypes can be laborious to capture adequately, and some phenotypes go unnoticed by the clinician (such as those only seen at home). Patients themselves are an eager and untapped source of information about symptoms and phenotypes, however, medical terminology is often perplexing to them, making it difficult to use resources like the HPO. Therefore, to support use of the HPO by patients directly, we have created a {\textquoteleft}layperson{\textquoteright} translation. Approximately 36\% of the HPO terms have at least one layperson synonym, 89\% of the diseases annotated to HPO have at least one HPO annotation with a layperson synonym, and 60\% of all disease annotations refer to HPO terms with lay translations. This coverage suggests that the layperson HPO would be useful in a diagnostic setting despite incomplete coverage. To evaluate the diagnostic utility of this lay translation, we created synthetic profiles ({\textquotedblleft}slim annotations{\textquotedblright}) for each annotated disease in the MONDO disease ontology and compared these slim annotations against the gold standard curated set. We also permuted these profiles by adding or removing annotations to determine how robust the lay annotation profiles might be in the face of missing or noisy data coming from patients. In order to evaluate the lay person profiles, we measured the semantic similarity between HPO gold standard annotations and the derived profiles (with and without noise added). 57\% of profiles scored 80\% similarity or higher, and 75\% of profiles scored 70\% similarity or higher. These results highlight the potential impact that the use of a patient-centered ontology view may have in clinical diagnostics for rare disease patients.}, keywords = {Medicine, Personalized medicine, phenotype, Translational science}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Melissa Haendel and Nicole Vasilevsky and Kayli Rageth and Sebastian Koehler and Peter Robinson and Julie McMurry and Kent Shefchek and Catherine Brownstein and Ingrid Holm and Chris Mungall} } @conference {ICBO_2018_74, title = {ICBO_2018_74: The many faces of ontological data}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {To be added}, keywords = {Bioinformatics, data visualization, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Melissa Haendel} } @conference {ICBO_2018_8, title = {ICBO_2018_8: Using Equivalence Axioms from the Mammalian Phenotype Ontology to Facilitate Phenotype and Expression Comparisons}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Comparisons of expression and phenotypes associated with a gene can enhance the understanding of gene function. These comparisons can be difficult to make due to differences in the ontologies used to annotate the data. Using equivalence axioms in the Mammalian Phenotype (MP) ontology and mappings between Uber-anatomy (UBERON) and EMAPA terms MGI has implemented gene expression + phenotype comparison matrices for genes in MGI. These matrices used the shared anatomical concepts between expression and phenotype ontologies to facilitate comparisons between annotations in these two domains.

}, keywords = {expression, mouse, phenotype}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_8.pdf }, author = {Susan Bello and Terry Hayamizu and Cynthia Smith and Martin Ringwald and The Mgi Software Group} } @conference {ICBO_2018_9, title = {ICBO_2018_9: A Prot{\'e}g{\'e} Plug-In for Test-Driven Ontology Development}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Ontology development is a hard and often error-prone process, which requires ontology authors to correctly express their domain knowledge in a formal language. One way to ensure the quality of the resulting ontology is to use test cases, similarly to the best practices in software development. For ontology development, test cases can be specified as statements describing expected and/or unwanted logical consequences of an ontology. However, verifying the test cases and identifying the ontology parts that cause their violation is a complex task, which requires appropriate tool support. In this demo, we present OntoDebug {\textendash} a plug-in for the Prot{\'e}g{\'e} editor {\textendash} that supports test-driven ontology development. OntoDebug can automatically verify whether the ontology satisfies all defined test cases. If any test case is violated, the plug-in assists the user in debugging and repairing the ontology in an interactive way. The plug-in asks a series of questions about the ontology to pinpoint the faulty axioms. Once a fault is repaired, all answers that the author provided in the interactive debugging session may be converted into test cases, thus preserving the additional knowledge, which can be used in future testing of the ontology.

}, keywords = {Fault Detection in Ontologies, Fault Localization in Ontologies, Fault Repair in Ontologies, Ontology Debugging, Prot{\'e}g{\'e} Plug-In, Test-Driven Ontology Development, User Interaction}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_9.pdf }, author = {Konstantin Schekotihin and Patrick Rodler and Wolfgang Schmid and Matthew Horridge and Tania Tudorache} }