@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_25, title = {ICBO_2018_25: OOPS: The Ontology of Plant Stress, A semi-automated standardization methodology}, 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 = {

Plant stress traits are an important breeding target for all crop species. Massive amounts of research dollars are spent combating plant diseases and nutrient deficiencies. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadata standards. One such standard is through the use of a standardized vocabulary, or ontology. This paper focuses on producing a semi-automated method to define, and label plant stresses using a combination of web scraping, and ontology design patterns. Standardizing the definitions, and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies.

}, keywords = {automation, data standards, nutrient deficiency, Ontology, plant pathology, Planteome, web scraping}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_25.pdf }, author = {Austin Meier and Marie-Ang{\'e}lique Laporte and Justin Elser and Laurel Cooper and Justin Preece and Pankaj Jaiswal and Jorrit Poelen} } @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_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_35, title = {ICBO_2018_35: Can a Convolutional Neural Network Support Auditing of NCI Thesaurus Neoplasm Concepts?}, 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 a Machine Learning methodology using a Convolutional Neural Network to perform a specific case of an ontology Quality Assurance, namely discovery of missing IS-A relationships for Neoplasm concepts in the National Cancer Institute Thesaurus (NCIt). The training step checking all {\textquotedblleft}uncles{\textquotedblright} of a concept is computationally intensive. To shorten the time and to improve the accuracy, we define a restricted methodology to check only uncles that are similar to each current concept. The restricted technique yields higher classification recall (compared to the unrestricted one) when testing against known errors found by domain experts who manually reviewed Neoplasm concepts in a prior study. The results are encouraging and provide impetus for further improvements to our technique.

}, keywords = {Abstraction Network, CNN, deep learning, machine learning, National Cancer Institute Thesaurus, Neoplasm Hierarchy, quality assurance}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_35.pdf}, author = {Hao Liu and Ling Zheng and Yehoshua Perl and James Geller and Gai Elhanan} } @conference {ICBO_2018_39, title = {ICBO_2018_39: TOCSOC: A temporal ontology for comparing the survival outcomes of clinical trials in oncology}, 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 outcome of clinical trials for cancer is typically summarized in terms of survival. However, different trials for the same disease may use different measures of survival, or use differing vocabulary to refer to the same outcome measure. This makes it harder to automate an objective comparison of treatments. We propose a temporal ontology of survival outcome measures that a) helps to standardize the vocabulary for reporting survival outcomes and b) makes it possible to automatically rank the relative efficacy of different treatments. The approach has been illustrated by examples from the oncology literature. The temporal ontology and the accompanying reasoner are freely available on Github (https://github.com/pdddinakar/TOCSOC)

}, keywords = {clinical trials, oncology, reasoning, survival outcome, temporal ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_39.pdf }, author = {Deendayal Dinakarpandian and Bhavish Dinakar and Michaela Liedtke and Mark Musen} } @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_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_50, title = {ICBO_2018_50: A Natural Language Processing Pipeline to extract phenotypic data from formal taxonomic descriptions with a Focus on Flagellate Plants}, 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 = {

Assembling large-scale phenotypic datasets for evolutionary and biodiversity studies of plants can be extremely difficult and time consuming. New semi-automated Natural Language Processing (NLP) pipelines can extract phenotypic data from taxonomic descriptions, and their performance can be enhanced by incorporating information from ontologies, like the Plant Ontology (PO) and the Plant Trait Ontology (TO). These ontologies are powerful tools for comparing phenotypes across taxa for large-scale evolutionary and ecological analyses, but they are largely focused on terms associated with flowering plants. We describe a bottom-up approach to identify terms from flagellate plants (including bryophytes, lycophytes, ferns, and gymnosperms) that can be added to existing plant ontologies. We first parsed a large corpus of electronic taxonomic descriptions using the Explorer of Taxon Concepts tool (http://taxonconceptexplorer.org/) and identified flagellate plant specific terms that were missing from the existing ontologies. We extracted new structure and trait terms, and we are currently incorporating the missing structure terms to the PO and modifying the definitions of existing terms to expand their coverage to flagellate plants. We will incorporate trait terms to the TO in the near future.

}, keywords = {flagellate plants, matrices, natural language processing, phenotypic traits, phylogeny, Plant Ontology, Plant Trait Ontology, taxonomic descriptions}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_50.pdf }, author = {Lorena Endara and Gordon Burleigh and Laurel Cooper and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte and Hong Cui} } @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_58, title = {ICBO_2018_58: Computational Classification of Phenologs Across Biological Diversity}, 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 = {

Phenotypic diversity analyses are the basis for research discoveries ranging from basic biology to applied research. Phenotypic analyses often benefit from the availability of large quantities of high-quality data in a standardized format. Image and spectral analyses have been shown to enable high-throughput, computational classification of a variety of phenotypes and traits. However, equivalent phenotypes expressed across individuals or groups that are not anatomically similar can pose a problem for such classification methods. In these cases, high-throughput, computational classification is still possible if the phenotypes are documented using standardized, language-based descriptions. Conversion of language-based phenotypes to computer-readable {\textquotedblleft}EQ{\textquotedblright} statements enables such large-scale analyses. EQ statements are composed of entities (e.g., leaf) and qualities (e.g., increased length) drawn from terms in ontologies. In this work, we present a method for automatically converting free-text descriptions of plant phenotypes to EQ statements using a machine learning approach. Random forest classifiers identify potential matches between phenotype descriptions and terms from a set of ontologies including GO (gene ontology), PO (plant ontology), and PATO (phenotype and trait ontology), among others. These candidate ontology terms are combined into candidate EQ statements, which are probabilistically evaluated with respect to a natural language parse of the phenotype description. Models and parameters in this method are trained using a dataset of plant phenotypes and curator-converted EQ statements from the Plant PhenomeNET project (Oellrich, Walls et al., 2015). Preliminary results comparing predicted and curated EQ statements are presented. Potential use across datasets to enable automated phenolog discovery are discussed.

}, keywords = {ontologies, phenologs, phenotypes, text mining}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_58.pdf }, author = {Ian Braun and Carolyn Lawrence-Dill} } @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_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_63, title = {ICBO_2018_63: GO-MAP Implements CAFA Tools: Improved Automated Gene Function Annotation for Plants}, 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 = {Maize is both a crop species and a model for genetics and genomics research. As such, maize GO annotations produced by the community data projects Gramene and Phytozome are widely used to derive hypotheses for both crop improvement and basic science. Our maize-GAMER project assessed existing maize GO annotations and to implement and test the performance of some of the most commonly used GO prediction tools (i.e., Reciprocal Best Hits and domain presence) alongside three of the top performing tools submitted for evaluation in the CAFA1 (Critical Assessment of protein Function Annotation) competition. All datasets were compared based on F-score using an independent gold-standard dataset (2002 GO annotations for 1,619 genes) provided by MaizeGDB. In addition to producing and comparing these individual GO annotation sets, we also combined the datasets we generated to produce a maize-GAMER aggregate annotation set. Compared to Gramene and Phytozome, the maize-GAMER aggregate set annotates more genes in the maize genome and assigns more GO terms per gene. In addition, the maize-GAMER dataset{\textquoteright}s functional assignments are comparable to Gramene and Phytozome overall (based on F-score). These findings have been published, and the maize-GAMER GO annotations are available via CyVerse and MaizeGDB. Here we review the methods and describe GO-MAP, the pipeline used to generate these datasets. GO-MAP has been containerized to facilitate gene function annotation for other plant proteomes and will be released via CyVerse in the very near future.}, keywords = {assessment, CAFA, function, gene ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Kokulapalan Wimalanathan and Carson Andorf and Iddo Friedberg and Carolyn Lawrence-Dill} } @conference {ICBO_2018_67, title = {ICBO_2018_67: Vocabularies, Ontologies, APIs, and Formats for Heterogeneous High Throughput Crop Phenotyping 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 = {The automation of sensors to measure crops is rapidly increasing in the size and complexity of the data that are available to scientists. The higher resolution, frequency, and diversity of information can enable new scientific discoveries and engineering approaches required to increase the efficiency and sustainability of our agricultural production systems. However, the size and complexity of these data makes it difficult to derive insights and actionable inference. These data often require contextual metadata including plant taxonomy, soil and weather, and agronomic and experimental conditions. While it is easy to agree on the principles of making data FAIR (findable, accessible, interoperable and reusable), it is a more substantial challenge to engineer such data in the absence of clear guidance. The landscape of standards, formats, vocabularies, and ontologies (hereafter, interfaces) that exist is difficult to navigate, and comprehensive specifications that cover the scope of crop phenotyping data are absent. Existing standards and conventions provide only a patchwork of coverage, making it difficult to standardize software and data interfaces. The TERRA Reference phenotyping platform (TERRA REF) is building a suite of open data and software to support advances in the use of crop sensing for breeding and precision agriculture. Although our data are large and diverse, we seek to make it FAIR, useful, and usable to the community of users who represent many science and engineering domains, including computer science, robotics, physics, and biology. This talk will describe our effort to identify, prioritize, and implement interfaces to these data, our efforts to build a community around shared data and software, and our focus on supporting existing software pipelines. It will conclude with a request for feedback on the role of ontologies can play in building a coherent interface to heterogeneous data.}, keywords = {APIs, Crop Plants, Environment, High Throughput Crop Phenotyping, High Throughput Phenotyping, Ontology, phenotype}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {David LeBauer and Craig Willis} } @conference {ICBO_2018_7, title = {ICBO_2018_7: A Quality Assurance Methodology for ChEBI Ontology Focusing on Uncommonly Modeled Concepts}, 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 Chemical Entities of Biological Interest (ChEBI) ontology is an important knowledge source of chemical entities in a biological context. ChEBI is large and complex, making it almost impossible to be error-free, given the scarce resources for quality assurance (QA). We present a methodology to locate concepts in ChEBI with a high probability of being erroneous. An Abstraction Network, which provides a compact summarization of an ontology, supports the methodology. By investigating a sample of ChEBI concepts, we show that uncommonly modeled concepts residing in small units of the Abstraction Network of ChEBI are statistically significantly more likely to have errors than other concepts. The finding may guide ChEBI ontology curators to focus their limited QA resources on such concepts to achieve a better QA yield. Furthermore, this study, combined with previous work, contributes to progress in showing that this methodology can be applied to a whole family of similar ontologies.

}, keywords = {ChEBI, chemical concept, chemical ontology, modeling error, quality assurance}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_7.pdf }, author = {Hao Liu and Ling Chen and Ling Zheng and Yehoshua Perl and James Geller} } @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 {177, title = {ICBO_2018_76: Designing and Building the IC-FOODS Foundry: Community, Technology, and Standards for a Semantic Web of Food}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, year = {2008}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, address = {Corvallis, OR}, abstract = {

It is becoming increasingly clear that precise and sustainable cultivation methods informed by air, water, soil, plant, animal, and climate/weather sensors are capable of repairing landscapes, improving carbon sequestration, and reducing toxic runoff while also providing enough food to feed the planet. Simultaneously, compounding evidence demonstrates that appropriately guided food habits for individuals, informed by multi-omic interrogations of biomolecules together with lifestyle/activity data, hold potential for preventing disease and improving quality of life while also extending it. Food remains a key socio-cultural artifact representing status, religion, and clanship while also acting as a confluence for exchange of stories and sharing in delight. These stories and experiences are increasingly written, cataloged, annotated, and shared through modern social media channels. Increases in traditional and social media coverage about food has heightened consumer awareness while increasing consumer expectations for knowledge about how their food is grown, made, delivered and served. Food producers, processors, distributors and consumers all need computable languages underpinning data stores to make them findable, accessible, discoverable, and reusable (FAIR). IC-FOODS, the International Consortium for Food Ontology Operability Data and Semantics (IC-FOODS) was established with a primary mission: the design, assembly, and build of the Internet of Food (IoF). At the heart of the IoF is the Semantic Web of Food, whose language core is an emerging set of core ontologies that span the environment\<\>agriculture\<\>food processing\<\>food distribution\<\>diet\<\>food consumption/sensory experience\<\>health knowledge spectrum. Key to its mission is the establishment of working groups dedicated to aligning extant vocabularies with each other and with emerging scientific and gastronomic concepts. Sanding on the shoulders of the OBO (Open Biological Ontologies) Foundry, IC-FOODS is developing an integrated Foundry dedicated to ontologies related to Food and Food Systems. Whereas OBO is a natural home for biological ontologies about food--several ontologies related to food are only partially biological in their nature. These include ontologies of food processing and machinery, food industry, and economics/trade. This presentation outlines the strategies for implementing workgroups and technologies for building the IC-FOODS Foundry together with tightly coupled integration with the OBO Foundry.

}, keywords = {Farm to Fork, Food Ontology, semantic web}, author = {Matthew Lange} }