@conference {ICBO_2018_10, title = {ICBO_2018_10: Standardization of the Histopathology Cancer Report: An Ontological Approach}, 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 = {

In recent years, the complexity of cancer pathology reporting has increased significantly. The pathology report covers not only general information such as the presence or absence of cancer, but includes a collection of specific parameters such as tumor size, grade, margin, lymphatic or vascular involvement as well as molecular testing e.g. proteomics and genomics (Figure 1). Soon, biomarkers and immune profiling will play an increasingly more important role in determining the eligibility for particular therapies, along with genetic predisposition and social risk factors. The increased use of digital pathology, which allows streamlined sharing of images, has highlighted the importance of clear communication of the information displayed in the pathology report. In the past years, significant effort has been devoted to redefining the way that histopathology report information is recorded. The College of American Pathologists (CAP) (http://www.cap.org/), a leading organization of board-certified pathologists, introduced synoptic cancer reports, a structured checklist to standardize clinical documentation. Despite continuous improvement and generation of electronic reports, formal representation [1] is still lacking. This lack of standardization limits the ability to integrate pathology information with other genomic and proteomic data and often results in loss of information.

}, keywords = {Cancer, Histopathology Cancer, Histopathology report, Ontology, pathology, Pathology standard, Tumor}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_10.pdf}, author = {Anna Maria Masci and Shannon McCall and Alessandro Racioppi and Helena Judge Ellis and Jihad S. Obeid and Barry Smith and Christian Stoeckert and Jie Zheng} } @conference {ICBO_2018_21, title = {ICBO_2018_21: From PO to GO and back}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {Our recent goal for the Plant Ontology (PO) is to have it integrated with then Gene Ontology (GO). The simplest aspect of this is to link morphological and anatomical images of structures in for PO terms with GO terms. This, also, would include images of in situs. By using a process of reciprocal illumination, we will be able to clarify and/or redefine PO terms, in particular. An example of this is the integument in the seed plants. Gymnosperms have one integument; whereas, Angiosperms (flowering plants) have two integuments, i.e., an inner integument surrounded by an outer integument. The question that arises is which of the two integuments in the Angiosperms is the equivalent of the single integument in then Gymnosperms? In Angiosperms, KANADI 1, 2 and 3 genes are expressed in the outer integument but not in the inner integument and in knock out mutants there is no outer integument. KANADI 1, 2 and 3 are found only in the Angiosperm outer integument. Thus, it appears that the inner integument of the Angiosperms is equivalent homologous) to the single integument of the Gymnosperms and the PO terms can be revised accordingly.}, keywords = {angiosperm, GO, gymnosperm, integument, Ontology, PO}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Dennis Stevenson and Cecilia Zumajo} } @conference {ICBO_2018_24, title = {ICBO_2018_24: eXtensible ontology development (XOD) using web-based Ontoanimal tools}, 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 eXtensible ontology development (XOD) strategy proposes four principles to support interoperable and robust ontology development. These principles include ontology term reuse, semantic alignment, design pattern usage, and community extensibility. In this software demo, we show how Ontoanimal tools (e.g., Ontofox, Ontodog, Ontorat, and Ontokiwi) can be used to support the implementation of these XOD principles. The development of the Cell Line Ontology (CLO) is used for the demonstration.

}, keywords = {Cell Line Ontology, CLO, eXtensible ontology development, Ontodog, Ontofox, Ontokiwi, Ontorat, XOD}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_24.pdf }, author = {Edison Ong and Yongqun He and Jie Zheng} } @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_52, title = {ICBO_2018_52: The integrative use of anatomy ontology and protein-protein interaction networks to study evolutionary phenotypic transitions}, 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 = {

Studying evolutionary phenotypic transitions, such as the fin to limb transition, is popular in evolutionary biology. The recent advances in next-generation technologies have accumulated large volumes of genomics and proteomics data, which can be used to analyze the genetic basis for evolutionary phenotypic transitions. Protein-protein interaction (PPI) networks can be used to predict candidate genes and identify gene modules related to evolutionary phenotypes; however, they suffer from low gene prediction accuracy. Therefore, an integrative framework was developed using PPI networks and anatomy ontology, which significantly improved the accuracy of network-based candidate gene predictions in zebrafish and mouse. This integrative framework will also be used to identify gene modules associated with the fin to limb transition and to study the changes in these modules which lead to the phenotypic change.

}, keywords = {anatomy ontology, data integration, gene prediction, network analysis, protein-protein interactions}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_52.pdf }, author = {Pasan Fernando and Erliang Zeng and Paula Mabee} } @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_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_66, title = {ICBO_2018_66: Visualization of N-ary Relationships}, 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 = {Biologists are interested in conducting gene expression analysis to detect co-expression clusters that are regulated by the same set of genes. Due to the involvement of a high number of genes, it becomes difficult to visualize the gene networks to make meaningful insights. By utilizing N-ary data visualization technique, we find the cliques (a complete graph of N genes) among the gene sets and visualize them as a two-dimensional complex (Nodes, edges and polygonal faces) and devise an optimal layout. Consequently, the biologists can easily recognize the cliques in each cluster, the cardinality of the cliques, predominant interactions and the degree of heterogeneity of the data from the visualization.}, keywords = {cliques, data visualization, graph layout, N-ary relationship}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Botong Qu and Prashant Kumar and Eugene Zhang and Pankaj Jaiswal and Laurel Cooper and Justin Elser and Yue Zhang} } @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} }