@conference {ICBO_2018_13, title = {ICBO_2018_13: The Identity and Mereology of Pathological Dispositions}, 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 = {

Diseases, risks of pathological processes and predispositions have been formalized as dispositions. The relations between those pathological dispositions, however, remain unclear. We apply here a recently developed theory of mereology and identity among dispositions to analyze such relations. In particular, we show how a framework for the identity of disposition leads to a disease being not only a disposition to a disease course, but also to each pathological process; how it avoids risk multiplicativism; and how a predisposition can be identified with a risk whose estimated probability is higher than for the risk of a reference class. We discuss how this makes predisposition always relative to a reference class, to a time-frame and to sources of risk estimates; and we clarify the nature of risk factors.

}, keywords = {disease, disposition, identity, mereology, predisposition, risk, risk factor}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_13.pdf}, author = {Adrien Barton and Olivier Grenier and Jean-Francois Ethier} } @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_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_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_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_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_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_43, title = {ICBO_2018_43: Domain Informational Vocabulary Extraction Experiences with Publication Pipeline Integration and Ontology Curation}, 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 will present updates on an ongoing project DIVE (Domain Informational Vocabulary Extraction), a system designed for extracting domain information from scientific publications. DIVE implements an ensemble of text mining methods for biological entity extraction from article text. DIVE also attempts use the co-occurrence patterns of these entities to establish probable relationships between them. DIVE also features an improved web interface for expert user curation of extracted information, thereby providing a means for a constantly growing and expert curated body of domain information for an article corpus. We also discuss our experiences from successful integration of DIVE with the publishing pipeline for two prominent Plant Biology Journals (The Plant Cell and Plant Physiology) from ASPB (American Society of Plant Biologists). The extracted results are embedded at the end of the final proof of the published article to enhance its accessibility and discoverability. Furthermore, DIVE tracks expert user curation actions on its web interface for future training and improvement of the entity detection algorithm.

}, keywords = {big data, Cyberinfrastructure, DIVE, machine learning, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_43.pdf }, author = {Amit Gupta and Weijia Xu and Pankaj Jaiswal and Crispin Taylor and Jennifer Regala} } @conference {ICBO_2018_5, title = {ICBO_2018_5: Semantic Integration of Heterogeneous Resources Based on Domain Ontology}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {Heterogeneity has become an important feature of information resources in the network environment. An integrated approach is needed to achieve the disclosure, association, and retrieval of multiple heterogeneous information resources. The traditional integration methods of information resource can solve the heterogeneous problems in terms of system level, syntax level, structure level, etc. The heterogeneity problems at semantic level will be solved under the support of domain ontology with enhanced semantic. This paper applies domain ontology to the semantic integration of heterogeneous information resources, designs a semantic integration model based on domain ontology, and focus on the problems that should be solved during the implementation of this model. This paper discusses the solutions for the problems and applies them to the process of semantic integration of open resources in the field of plant diversity. The application effect is verified to support the service scenes such as semantic retrieval and knowledge browsing in specific subject areas.}, keywords = {domain ontology, open resources, plant diversity, semantic heterogeneity, semantic integration}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Jinjing Guo and Huiling Ren and Jinxia Huang} } @conference {ICBO_2018_6, title = {ICBO_2018_6: PHO: The Pharmacognosy 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 = {The discipline of pharmacognosy was named and defined in the early 19th century and consists primarily in the study of materia medica, or drugs. Its name, a derivation of the Greek pharmakon (medicine/drug) and ginosko (to know through experience) embraces a broad disciplinary range; current practice involves the investigation of natural products, the material produced by or derived from living organisms. It is a meta-discipline located at the frontiers of numerous sciences not limited to biology, chemistry, physics, and pharmacy. Pharmacognosy not only benefits from the technical and conceptual advances across these disciplines; it endows them with new study subjects and furnishes them with new tools and approaches. However, pharmacognosy has yet to articulate the diversity of its practices and procedures in a systematic, well-defined and logical manner, in suach a way as to participate in the lingua franca that ontologies can provide. Through the creation of a Pharmacognosy Ontology, we hope to contribute to this goal and consolidate the existing bridges between pharmacognosy and the many disciplines it is interacting with and thus contribute to a more profound understanding of the natural world.}, keywords = {meta-discipline ontology, natural product, pharmacognosy}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Jonathan Bisson and James Graham and Guido Pauli} } @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_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_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 {ICBO_2018_73, title = {ICBO_2018_73: Taxa, metacoder, poppr and vcfR: Four packages for parsing, visualization, and manipulation of genetic, genomic and metagenomic data in R}, 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 = {Contemporary population genomic microbiome research are producing complex and large datasets that are difficult to manipulate and visualize. High throughput DNA sequencing projects typically result in files containing genetic variants in the Variant Call Format (VCF). These files are large and not strictly tabular, presenting an issue to investigators who wish to work with this data in the R environment. We created the package vcfR to facilitate exploration of VCF data in R. vcfR uses Rcpp to implement C++ functions, allowing R users to take advantage of the performance of compiled code without the user needing to know about the compiled code. This facilitates efficient use of VCF data in R. New users who are unfamiliar with VCF data can now explore the data to learn about their data. Quality control steps can be conducted. In addition, manipulations, such as subsetting samples or variants, can be performed. We provide conversion functions so analyses in the popular R genetics packages adegenet, poppr, ape and pegas. Analysis of population differentiation and copy number variation can be performed directly in vcfR. Our package vcfR facilitates efficient use, manipulation, and analysis of VCF data and integrates this data into the existing ecosystem of R genetic analysis and graphics packages. The taxa package provides a set of classes for the storage and manipulation of taxonomic data. Classes range from simple building blocks to project-level objects storing multiple user-defined datasets mapped to a taxonomy. It includes parsers that can read in taxonomic information in nearly any form. We have also developed the metacoder package for visualizing hierarchical data. Metacoder implements a novel visualization called heat trees that use the color and size of nodes and edges on a taxonomic tree to quantitatively depict up to 4 statistics. This allows for rapid exploration of data and information-dense, publication-quality graphics. This is an alternative to the stacked barcharts typically used in microbiome research. These complementary tools provide a new resource for analyzing hierarchical population genomic and microbiome data in R.}, keywords = {Bioinformatics, Metagenome, Microbiome, Population genetics, Population genomics, Taxonomy, Variant Call}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Niklaus Grunwald} } @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} }