@conference {ICBO_2018_11, title = {ICBO_2018_11: Planteome \& BisQue: Automating Image Annotation with Ontologies using Deep-Learning Networks}, 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 field of computer vision has recently experienced tremendous progress due to advances in deep learning. This development holds particular promise in applications for plant research, due to a significant increase in the scale of image data harvesting and a strong field-driven interest in the automated processing of observable phenotypes and visible traits within agronomically important species. Parallel developments have occurred in semantic computing; for example, new ontologies have been initiated to capture plant traits and disease indicators. When combined with existing segmentation capabilities, it is possible to conceptualize software applications that give researchers the ability to analyze large quantities of plant phenotype image data, and to auto-annotate that data with meaningful, computable semantic terminology. We have previously reported on a software application that integrates segmentation and ontologies, but lacked the ability to manage very high-resolution images, and also lacked a database platform to allow for high-volume storage requirements. We have also previously reported our migration of the AISO user-guided segmentation feature to a BisQue (Bio-Image Semantic Query User Environment) module to take advantage of its increased power, ability to scale, secure data management environment, and collaborative software ecosystem. Neither AISO nor our initial BisQue implementation possessed a machine-learning component for interpreting (parts of) images. Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy. We have therefore implemented two deep-learning prototypes: a coarse classification module for plant object identification (i.e. flower, fruit) and a fine-grained classification module that focuses on plant traits (e.g. reticulate vs. parallel venation, tip shape). Both classification models return results mapped to ontology terms as a form of annotation enrichment. This current version of the Planteome Deep Segmenter module combines image classification with optional guided segmentation and ontology annotation. We have most recently run the module on local Planteome BisQue client services, and are currently working with CyVerse to install a hosted version on their BisQue client service.

}, keywords = {annotation, convolutional neural networks, deep learning, image analysis, machine learning, Ontology, segmentation}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_11.pdf}, author = {Dimitrios Trigkakis and Justin Preece and Austin Meier and Justin Elser and Kris Kvilekval and Dmitry Fedorov and B.S. Manjunath and Pankaj Jaiswal and Sinisa Todorovic} } @conference {ICBO_2018_15, title = {ICBO_2018_15: Quality Assurance of Ontology Content Reuse}, 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 = {

Building ontologies is difficult and time-consuming. As such, content reuse has been promoted as an important guiding principle in ontology development. Reusing content from other ontologies can reduce the overall effort involved in new ontology construction and provide better alignment with existing knowledge modeling. However, reuse is not a panacea, and it comes with its own attendant difficulties. In this paper, we investigate some common quality assurance issues associated with reuse, such as duplicated content and versioning problems. Some heuristic-based approaches are proposed for analyzing ontologies for these kinds of quality assurance issues. An analysis is carried out on a sample of the large collection of BioPortal-hosted ontologies, many of which employ reuse. The findings indicate that curators and authors, particularly those new to the reuse process, should be on the alert when developing an ontology with reused content to avoid introducing problems into their own ontologies.

}, keywords = {BioPortal, modeling, Ontology, ontology quality assurance, ontology reuse}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_15.pdf }, author = {Michael Halper and Christopher Ochs and Yehoshua Perl and Sivaram Arabandi and Mark Musen} } @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_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_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_42, title = {ICBO_2018_42: ELIXIR Interoperability: Standardisation of identifiers, schemas, and ontologies for scientific communities}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {ELIXIR (https://www.elixir-europe.org) unites Europe{\textquoteright}s leading life science organisations in managing the increasing volume of data being generated by publicly funded research. ELIXIR operates in a Hub-Node model with the Hub coordinating the activities undertaken by the Nodes via five technical platforms: Compute, Data, Interoperability, Tools, and Training to build a sustainable and foundational framework for Life Science informatics communities. This is done by establishing strategic guidelines underlying scientific operations across ELIXIR{\textquoteright}s twenty-one Node members (as of May 2018). The ELIXIR Interoperability Platform (EIP) has been established to deal with the challenge of delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data across the different levels of complexity and variety of life science data types: across the datasets, data catalogues, data tools and services; across the multitude of biological disciplines and geographical or organisational boundaries. The EIP aims to make available the services and resources needed to make data FAIR. These strategies will be founded on FAIR Principles. Ontologies are a crucial component of the EIP strategy, with OBO Foundry Ontologies recognised as reference resource, and leveraged through resources such as Ontology Lookup Service, which provides additional user services for search and annotation. EIP is currently engaged in addressing specific community-derived use cases where incorporating ontological information is essential: rare disease, marine metagenomics, and plant informatics. These require reference ontologies such as Human Phenotype Ontology (HP), Disease Ontology (DO), Cell Ontology (CL), and UBERON.}, keywords = {Bioschemas, FAIR, identifiers, Interoperability, linked-data, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Sirarat Sarntivijai and Nick Juty and Carole Goble and Helen Parkinson and Chris Evelo and Jerry Lanfear and Niklas Blomberg} } @conference {ICBO_2018_53, title = {ICBO_2018_53: Visualization of gene expression and expression as a phenotype with the XPO, XAO and DO using a combination of experimental data sources}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

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

}, keywords = {biocuration, gene expression, genomics, human disease modeling, model organism database, xenopus anatomy ontology, xenopus phenotype ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_53.pdf }, author = {Troy Pells and Malcolm Fisher and Erik Segerdell and Joshua Fortriede and Kevin Burns and Stanley Chu and Praneet Chaturvedi and Christina James-Zorn and Vaneet Lotay and Mardi Nenni and V.G. Ponferrada and Dong Zhou Wang and Ying Wang and Kamran Karimi and Peter Vize and Aaron Zorn} } @conference {ICBO_2018_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_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} }