@conference {ICBO_2018_10, title = {ICBO_2018_10: Standardization of the Histopathology Cancer Report: An Ontological Approach}, 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 = {

In recent years, the complexity of cancer pathology reporting has increased significantly. The pathology report covers not only general information such as the presence or absence of cancer, but includes a collection of specific parameters such as tumor size, grade, margin, lymphatic or vascular involvement as well as molecular testing e.g. proteomics and genomics (Figure 1). Soon, biomarkers and immune profiling will play an increasingly more important role in determining the eligibility for particular therapies, along with genetic predisposition and social risk factors. The increased use of digital pathology, which allows streamlined sharing of images, has highlighted the importance of clear communication of the information displayed in the pathology report. In the past years, significant effort has been devoted to redefining the way that histopathology report information is recorded. The College of American Pathologists (CAP) (http://www.cap.org/), a leading organization of board-certified pathologists, introduced synoptic cancer reports, a structured checklist to standardize clinical documentation. Despite continuous improvement and generation of electronic reports, formal representation [1] is still lacking. This lack of standardization limits the ability to integrate pathology information with other genomic and proteomic data and often results in loss of information.

}, keywords = {Cancer, Histopathology Cancer, Histopathology report, Ontology, pathology, Pathology standard, Tumor}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_10.pdf}, author = {Anna Maria Masci and Shannon McCall and Alessandro Racioppi and Helena Judge Ellis and Jihad S. Obeid and Barry Smith and Christian Stoeckert and Jie Zheng} } @conference {ICBO_2018_12, title = {ICBO_2018_12: Transforming and Unifying Research with Biomedical Ontologies: The Penn TURBO project}, 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 Penn TURBO (Transforming and Unifying Research with Biomedical Ontologies) project aims to accelerate finding and connecting key information from clinical records for research through semantic associations to the processes that generated the clinical data. Major challenges to using clinical data for research are integrating data from different sources which may contain multiple references to the same entity (e.g., person, health care encounter) and incomplete or conflicting information (e.g., gender, BMI). There is also the need to track the provenance of information used when making decisions on what is the actual phenotype of a person. We take a realism-based ontology approach to address these problems through transformation and instantiation of clinical data with an OBO-Foundry based application ontology in a semantic graph database. We have developed an application stack and used it on an 11,237 whole exome sequencing patient cohort capturing key demographics, diagnosis codes, and prescribed medications. The anticipated payoff is to be able to make use of inferencing provided by the semantics to classify and search for instances of people and specimens with desired characteristics.

}, keywords = {clinical data, diagnosis codes, OBO Foundry, prescriptions, realism-based ontology, referent tracking}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_12.pdf}, author = {Christian Stoeckert and David Birtwell and Hayden Freedman and Mark Miller and Heather Williams} } @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_20, title = {ICBO_2018_20: KNowledge Acquisition and Representation Methodology (KNARM)}, 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 = {

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. In-depth analyses making use of various data types and data sources, and extracting knowledge has become one of the many challenges with this big data. This is especially the case in life-sciences where simplification and flattening of diverse data types often leads to incorrect predictions. Effective applications of big data approaches in the life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding overly extreme simplification, such as reducing various biological data types to the gene level. A major challenge in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited and significant human expertise is required. In this research, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We also examplify how KNARM was applied on three ontologies: BioAssay Ontology (BAO), LINCS FramEwork Ontology (LIFE) ,and Drug Target Ontology (DTO) built for three different projects: BioAssay Ontology, Library of Integrated Network-Based Cellular Signatures (LINCS), and Illuminating the Druggable Genome (IDG), and how the methodology help the ontologies work together in complex queries.

}, keywords = {big data, ontology building methodology, semi-automated ontology building}, author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Stephan Schurer and Ubbo Visser} } @conference {ICBO_2018_21, title = {ICBO_2018_21: From PO to GO and back}, 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 recent goal for the Plant Ontology (PO) is to have it integrated with then Gene Ontology (GO). The simplest aspect of this is to link morphological and anatomical images of structures in for PO terms with GO terms. This, also, would include images of in situs. By using a process of reciprocal illumination, we will be able to clarify and/or redefine PO terms, in particular. An example of this is the integument in the seed plants. Gymnosperms have one integument; whereas, Angiosperms (flowering plants) have two integuments, i.e., an inner integument surrounded by an outer integument. The question that arises is which of the two integuments in the Angiosperms is the equivalent of the single integument in then Gymnosperms? In Angiosperms, KANADI 1, 2 and 3 genes are expressed in the outer integument but not in the inner integument and in knock out mutants there is no outer integument. KANADI 1, 2 and 3 are found only in the Angiosperm outer integument. Thus, it appears that the inner integument of the Angiosperms is equivalent homologous) to the single integument of the Gymnosperms and the PO terms can be revised accordingly.}, keywords = {angiosperm, GO, gymnosperm, integument, Ontology, PO}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Dennis Stevenson and Cecilia Zumajo} } @conference {ICBO_2018_27, title = {ICBO_2018_27: Semantic Interoperability: Challenges and Opportunities in Cell Type Knowledge Representation}, 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 Cell Atlas (HCA) and the California Institute for Regenerative Medicine{\textquoteright}s Center of Excellence in Stem Cell Genomics (CESCG) are identifying novel cell types at a rapid pace using single cell RNA sequencing (scRNAseq). To maximize the scientific return on these discoveries, it will be critical that the data derived from these studies is translated into biological knowledge that is findable, accessible, interoperable and reproducible (FAIR). To achieve this objective, we are developing a provisional cell type ontology (pCL) where the data being gathered from experimental work is represented in a standard semantic format that can be exchanged, retrieved, and inferred over using standard approaches and tools.

}, keywords = {cell type, CL Ontology, GABAergic, glutamatergic, HCA, Interoperability, pCL, scRNAseq}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Richard Scheuermann and Brian Aevermann and Mohamed Keshk} } @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_29, title = {ICBO_2018_29: Graph Summary of Ontology-based Annotations}, 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 = {It is desirable to learn about gene function in ever more detail and to communicate the information using the vocabularies of ontologies. However, rich annotations can exacerbate the challenge of intuitive comprehension of that complex knowledge by a human. We developed a graph-based tool called SObA (Summary of Ontology-based Annotations) to simplify and summarize complex annotations while allowing users to drill down into details easily by following the graph paths.In contrast to fixed-category summaries, such as ribbons or slims, for SObA, ontology graphs are trimmed dynamically, strictly dictated by available annotations. By following a set of simple rules, any node and path in the graph that is redundant or less informative is removed. Furthermore, visual cues to important nodes are provided by node color and size, which are determined by the type and amount of annotations. The SObA graph was implemented using the Cytoscape Javascript library, making it easy for users to traverse the graph at multiple levels of detail.SObA can be applied on any annotations that use DAG (directed acyclic graph) ontologies. We have implemented it on C. elegans phenotype annotations and on Gene Ontology annotations of many species. We are exploring the feasibility of applying SObA to summarize annotations of post-composed ontologies such as the mammalian phenotype ontology.}, keywords = {DAG, GO, Ontology, phenotype, summary graph}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Raymond Lee and Juancarlos Chan and Christian Grove and Paul Sternberg} } @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_32, title = {ICBO_2018_32: Ontolobridge {\textendash} A Semi-Automated Ontology Update Request System for Better FAIR-ifying BioAssay Ontology, Drug Target Ontology, and LINCS 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 = {Ontologies are products that are becoming more relevant for data science as the need for standardized vocabulary and meta-data is increasing. However, if they want to stay relevant, ontologies have a constant need for evolving, especially in domains that involve dynamic data, like life-sciences data. Based on the need pointed out by domain experts for updating and/or requesting ontology terms while annotating BioAssay Protocols, we are developing a semi-automated technology that will allow users to request new terms and update existing ones easier. This need was pointed out by domain experts who are using CDD{\textquoteright}s new tool BioAssay Express (BAE). BAE allows users to annotate their bioassays in a semi-automated and standardized fashion using the highly-accessed ontologies (BioAssay Ontology (BAO), Gene Ontology (GO), Disease Ontology (DOID), and Drug Target Ontology (DTO) among others) in the background. Our goal in the Ontolobridge project is to help various users of BAE (researchers performing curation, dedicated curators, IT specialists, ontology owners, and librarians/repositories) request and update the existing vocabulary provided by BAO in a semi-automated way, with a user-friendly interface. Furthermore, APIs and tools including templates from CEDAR will be created in order to would allow users to request new ontology terms or changes to existing terms easily during the annotation process. In this way, we{\textquoteright}re aiming to increase the Findability, Interoperability, Accessibility, and Reproducibility (FAIR) of above mentioned ontologies and BioAssay Protocols better.}, keywords = {annotation, big data, BioAssay Express, BioAssay Ontology, CEDAR, FAIR, FAIR data, life sciences big data, ontology term request, semi-automated ontology updates}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Alex Clark and John Graybeal and Daniel Cooper and John Turner and Michael Dorf and Mark Musen and Barry Bunin and Stephan Schurer} } @conference {ICBO_2018_42, title = {ICBO_2018_42: ELIXIR Interoperability: Standardisation of identifiers, schemas, and ontologies for scientific communities}, 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 = {ELIXIR (https://www.elixir-europe.org) unites Europe{\textquoteright}s leading life science organisations in managing the increasing volume of data being generated by publicly funded research. ELIXIR operates in a Hub-Node model with the Hub coordinating the activities undertaken by the Nodes via five technical platforms: Compute, Data, Interoperability, Tools, and Training to build a sustainable and foundational framework for Life Science informatics communities. This is done by establishing strategic guidelines underlying scientific operations across ELIXIR{\textquoteright}s twenty-one Node members (as of May 2018). The ELIXIR Interoperability Platform (EIP) has been established to deal with the challenge of delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data across the different levels of complexity and variety of life science data types: across the datasets, data catalogues, data tools and services; across the multitude of biological disciplines and geographical or organisational boundaries. The EIP aims to make available the services and resources needed to make data FAIR. These strategies will be founded on FAIR Principles. Ontologies are a crucial component of the EIP strategy, with OBO Foundry Ontologies recognised as reference resource, and leveraged through resources such as Ontology Lookup Service, which provides additional user services for search and annotation. EIP is currently engaged in addressing specific community-derived use cases where incorporating ontological information is essential: rare disease, marine metagenomics, and plant informatics. These require reference ontologies such as Human Phenotype Ontology (HP), Disease Ontology (DO), Cell Ontology (CL), and UBERON.}, keywords = {Bioschemas, FAIR, identifiers, Interoperability, linked-data, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Sirarat Sarntivijai and Nick Juty and Carole Goble and Helen Parkinson and Chris Evelo and Jerry Lanfear and Niklas Blomberg} } @conference {ICBO_2018_53, title = {ICBO_2018_53: Visualization of gene expression and expression as a phenotype with the XPO, XAO and DO using a combination of experimental data sources}, 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 = {

Xenbase (www.xenbase.org) is a knowledge base for researchers and biomedical scientists that employ Xenopus (X. laevis and X. tropicalis) as a model organism in gaining a deeper understanding of developmental and disease processes. Through expert curation and automated data provisioning from various sources this MOD (model organism database) strives to integrate the Xenopus body of knowledge together with the visualization of biologically significant interactions. We present a vision for the usage of various ontologies that facilitate the visualization of gene expression from a combination of experimental data sources and the linking thereof back to human disease modeling. In keeping with the philosophy to foster greater insight through the use of visualization techniques that bring together data from different sources and show trends in the large body of experimental and curated data in Xenbase. The approach has been taken to represent key developmental stages and selected embryonic/adult tissue anatomy terms with a modified heat map rendering. This gives us the combined results from the in-situ gene expression summary as well as normalized TPM read counts (Sessions et al.) and UMI counts from the single cell experiment data (Peshkin et al.). Through the acquisition of RNA-seq and ChIP-seq Xenopus data from GEO and subsequent processing through a bioinformatics pipeline to obtain average TPM read counts and differential expression readings. This enables the subsequent construction and visualization of EaP (Expression as a Phenotype) in conjunction with the XPO (Xenopus Phenotype Ontology) and DO (Disease Ontoloty). This data manipulation has practical application in making the information accessible from gene pages and linking back to the source (eg:article/image). If you use Xenbase resources in your research please consider citing us, for example Nucleic Acids Res. 2018 46(D1):D861-D868.

}, keywords = {biocuration, gene expression, genomics, human disease modeling, model organism database, xenopus anatomy ontology, xenopus phenotype ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_53.pdf }, author = {Troy Pells and Malcolm Fisher and Erik Segerdell and Joshua Fortriede and Kevin Burns and Stanley Chu and Praneet Chaturvedi and Christina James-Zorn and Vaneet Lotay and Mardi Nenni and V.G. Ponferrada and Dong Zhou Wang and Ying Wang and Kamran Karimi and Peter Vize and Aaron Zorn} } @conference {ICBO_2018_57, title = {ICBO_2018_57: An Ontology For Formal Representation Of Medication Adherence-Related Knowledge: Case Study In Breast Cancer}, 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 = {

Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medication-adherence behavior data that poses a challenge to humans and today{\textquoteright}s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior{\textemdash}{\textquotedblleft}Medication Adherence Behavior Ontology{\textquotedblright} (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Prot{\'e}g{\'e}. The MAB-ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories.

}, keywords = {adherence, adjuvant endocrine therapy, adjuvant hormonal therapy, aromatase inhibitors, Ontology, tamoxifen}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_57.pdf }, author = {Suhila Sawesi and Josette Jones and William Duncan} } @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_71, title = {ICBO_2018_71: Comparative functional analysis in plants with Gramene}, 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 = {Gramene (http://www.gramene.org) is an integrated resource for comparative functional analysis in plants, and provides researchers with access to 53 genomes and pathways for 75 plant species. Gramene includes powerful phylogenetic approaches, such as protein-based gene trees with stable IDs and whole-genome DNA alignments, enabling comparison across plant species. Gramene also provides integrated search capabilities and interactive views to locate and visualize gene features, gene neighborhoods, phylogenetic trees, genetic variation, gene expression profiles, pathways, ontology associations, and curated and orthology-projected pathways. Gramene builds upon Ensembl and Reactome software, and is committed to open access and reproducible science based on FAIR principles. Gramene is supported by an NSF grant IOS-1127112, and from USDA-ARS (1907-21000-030-00D).}, keywords = {annotation, comparative analysis, functional genomics, ontology associations, pathways, plants}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Parul Gupta and Justin Preece and Pankaj Jaiswal and Marcela K. Tello-Ruiz and Sharon Wei and Sushma Naithani and Andrew Olson and Joshua Stein and Yinping Jiao and Bo Wang and Sunita Kumari and Young Koung Lee and Vivek Kumar} } @conference {174, title = {ICBO_2018_75: ECOCORE: An ontology for core ecological concepts}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The ontologies in the biological and biomedical domain represent entities from a broad spectrum of the life sciences such as molecules, diseases, phenotypes, and environments. However, until now, ontologies representing ecological concepts, such as trophic modes like autotrophy and insectivory, have been missing. In response to the needs of aggregators such as the Encyclopedia of Life and DataONE, we have developed an ontology of core ecological concepts, ECOCORE. The initial focus of development for ECOCORE is trophic dynamics. An example demonstrating this initial focus is the autotroph class, which is a subclass of organism. It is equivalent to organism and ({\textquoteleft}capable of{\textquoteright} some autotrophy). Autotrophy is a process. ECOCORE development calls on classes from many OBO ontologies, but involves specifically important collaborations with the Environment Ontology, the Behavior Ontology, and the Relations Ontology. ECOCORE development is open to community participation at the GitHub site https://github.com/EcologicalSemantics/ecocore. This presentation will describe ECOCORE and show some of its uses.

}, keywords = {biodiversity, ecology, evolutionary biology, Ontology, ontology development, trophic dynamics}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Anne Thessen and Pier Luigi Buttigieg and Ramona Walls and Jennifer Verdolin and Katja Schulz} } @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} }