@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_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_41, title = {ICBO_2018_41: Formalizing the Representation of Immune Exposures for Human Immunology Studies}, 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 immunology studies typically examine how immune exposures associated with vaccinations, infectious, allergic or autoimmune diseases, or transplantations perturb the immune system with the goal to develop diagnostic tools and therapeutic interventions. While there are established approaches to formally represent the experimental data generated in such studies, which often comprises gene expression data, flow cytometry data, or serology data, the description of the immune exposures themselves is not well standardized. We here present a formal approach to represent immune exposures at a high level of granularity. We capture the exposure process (e.g. {\textquoteleft}vaccination{\textquoteright} or {\textquoteleft}occurrence of allergic disease{\textquoteright}), exposure material (e.g. {\textquoteleft}Tdap vaccine{\textquoteright} or {\textquoteleft}House dust mite{\textquoteright}), and the associated disease name and stage (e.g. {\textquoteleft}allergic rhinitis{\textquoteright} and {\textquoteleft}chronic{\textquoteright}). This representation scheme has been used successfully in the IEDB and an extended version has been adopted by HIPC to capture studies in ImmPort. We are reporting here on this scheme, our ongoing attempts to map the terms used to existing ontologies, and the challenges encountered.

}, keywords = {disease, Exposure, Immunology, Standardization}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_41.pdf }, author = {Randi Vita and Bjoern Peters and James Overton and Kei-Hoi Cheung and Steven Kleinstein} } @conference {ICBO_2018_47, title = {ICBO_2018_47: On the statistical sensitivity of semantic similarity metrics}, 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 = {

Measuring the semantic similarity between objects that have been annotated with ontological terms is fundamental to an increasing number of biomedical applications, and several different ontologically-aware semantic similarity metrics are in common use. In some of these applications, only weak semantic similarity is expected for biologically meaningful matches. In such cases, it is important to understand the limits of sensitivity for these metrics, beyond which biologically meaningful matches cannot be reliably distinguished from noise. Here, we present a statistical sensitivity comparison of five common semantic similarity metrics (Jaccard, Resnik, Lin, Jiang \& Conrath, and Hybrid Relative Specificity Similarity) representing three different kinds of metrics (Edge based, Node based, and Hybrid) and four different methods of aggregating individual annotation similarities to estimate similarity between two biological objects - All Pairs, Best Pairs, Best Pairs Symmetric, and Groupwise. We explore key parameter choices that can impact sensitivity. To evaluate sensitivity in a controlled fashion, we explore two different models for simulating data with varying levels of similarity and compare to the noise distribution using resampling. Source data are derived from the Phenoscape Knowledgebase of evolutionary phenotypes. Our results indicate that the choice of similarity metric, along with different parameter choices, can substantially affect sensitivity. Among the five metrics evaluated, we find that Resnik similarity shows the greatest sensitivity to weak semantic similarity. Among the ways to combine pairwise statistics, the Groupwise approach provides the greatest discrimination among values above the sensitivity threshold, while the Best Pairs statistic can be parametrically tuned to provide the highest sensitivity. Our findings serve as a guideline for an appropriate choice and parameterization of semantic similarity metrics, and point to the need for improved reporting of the statistical significance of semantic similarity matches in cases where weak similarity is of interest.

}, keywords = {annotation granularity, curation, Ontology, phenotype, semantic similarity}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_47.pdf }, author = {Prashanti Manda and Todd Vision} } @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_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_62, title = {ICBO_2018_62: Reasoning over anatomical homology in the Phenoscape KB}, 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 Phenoscape project (www.phenoscape.org) has semantically annotated the features of species from the comparative literature, enabling links between candidate genes and novel species phenotypes for which they might be responsible. To enable discovery of homologous phenotypes and associated genes, we incorporated machine-reasoning with knowledge about homology into the Phenoscape Knowledgebase (KB). We show that with homology reasoning enabled, the results of database queries can be expanded to incorporate shared evolutionary history. We discuss the challenges in developing a logical model of homology assertions and implications for database queries, as well as theoretical entailment and practical performance tradeoffs between alternative models.

}, keywords = {anatomy ontology, evolution, homology, phenotypes, reasoning}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_62.pdf }, author = {Paula Mabee and James Balhoff and Wasila Dahdul and Hilmar Lapp and Christopher Mungall and Todd Vision} } @conference {ICBO_2018_64, title = {ICBO_2018_64: Development and implementation of the Sickle Cell Disease 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 = {Sickle Cell Disease Ontology (SCDO), is a standardized and human and machine-readable resource that represents terminology about Sickle Cell Disease (SCD), for use by researchers, patients and clinicians. This is a collaborative effort to unambiguously describe all SCD concepts and unifying up-to-date knowledge in sickle cell disease.}, keywords = {Hemoglobinopathy, Ontology, Sickle Cell Disease, Translational research}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Gaston Mazandu and Sickle Cell Disease Ontology Working Group and Nicole Vasilevsky and Ambroise Wonkam and Nicola Mulder} } @conference {ICBO_2018_65, title = {ICBO_2018_65: An ontology-driven data capture and catalogue framework for the development of research and clinical databases in Sickle Cell Disease}, 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 designed an ontology-based Research Electronic Data Capture (REDCap) database of integrated medical and environmental information for Sickle Cell Disease patients. This database integrates data elements in patient clinical records or case report forms (CRFs) across multiple sites and contributes to the unification and standardization of clinical data collection instruments for SCD patient and enhances clinical decision making process and patient care.}, keywords = {Ontology, REDCap, Sickle Cell Disease}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Khuthala Mnika and Sickle Cell Disease Ontology Working Group and Nicole Vasilevsky and Ambroise Wonkam and Nicola Mulder} } @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 {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} }