@conference {ICBO_2018_19, title = {ICBO_2018_19: Current Development in the Evidence and Conclusion Ontology (ECO)}, 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 Evidence \& Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology term for a molecular function to a protein), the type of evidence supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they support. These include the Ontology for Microbial Phenotypes and the Gene Ontology. In addition, we continue to support user-level class requests through our GitHub issue tracker. To facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow. ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as the ECO web page (http://evidenceontology.org/).

}, keywords = {biocuration, evidence, gene annotation, ontology development, ontology mapping}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_19.pdf }, author = {Rebecca Tauber and James B. Munro and Suvarna Nadendla and Binika Chunara and Marcus C. Chibucos and Michelle Giglio} } @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_37, title = {ICBO_2018_37: Ontology-Enhanced Representations of Non-image Data in The Cancer Image Archive}, 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 Cancer Image Archive (TCIA) hosts over 11 million de-identified medical images related to cancer for research reuse. These are organized around DICOM-format radiological collections that are grouped by disease type, modality, or research focus. Many collections also include diverse non-image datasets in a variety of formats without a common approach to representing the entities that the data are about. This paper describes work to make these diverse non-image data more accessible and usable by transforming them into integrated semantic representations using Open Biomedical Ontologies, highlights obstacles encountered in the data, and presents detailed representations data found in select collections.

}, keywords = {Cancer, imaging, ontology development, semantics}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_37.pdf }, author = {Jonathan Bona and Tracy Nolan and Mathias Brochhausen} } @conference {ICBO_2018_45, title = {ICBO_2018_45: Formal Ontological Framework for representation of Food Phenotype, Sensation, Perception Tractable Flavor}, 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 = {

Among all sensory sciences, flavour remains a wicked problem. Sight, sound, and touch have all been digitized, and vast resources around their computation exist. While the biological basis for food consumption is primarily to nourish bodily functions, it fulfills a greater second function of sensory pleasure. Flavor, and the pleasure it engenders, is the primary driver of food choice. Moving toward a semantic web of food that enables personalization of food and flavor experiences requires an interoperable ontological model of flavor. This paper proposes a framework of several ontologies to model a comprehensive view of flavor, by partitioning it into three interoperable matrices of interacting variables: objective characteristics of food, subjective sensory experience, and interpretive communication of that experience. The objective matrix details the properties and behaviour of food molecules. The subjective matrix represents the multilayered and highly individualised consumption and sensory perception variables. The interpretative layer deals with the communication and language used to describe the food experience. Together these three matrices represent an initial ontological model for the flavor and sensory experience portion of the emerging semantic web of food.

}, keywords = {digital model, Flavour, Food Phenotype, formal ontologies, organoleptic, semantic web}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_45.pdf }, author = {Tarini Naravane and Matthew Lange} } @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_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} }