The Evaluation of Ontologies: Editorial Review Vs
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Genome Informatics 4–8 September 2002, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
Comparative and Functional Genomics Comp Funct Genom 2003; 4: 509–514. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.300 Feature Meeting Highlights: Genome Informatics 4–8 September 2002, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK Jo Wixon1* and Jennifer Ashurst2 1MRC UK HGMP-RC, Hinxton, Cambridge CB10 1SB, UK 2The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK *Correspondence to: Abstract Jo Wixon, MRC UK HGMP-RC, Hinxton, Cambridge CB10 We bring you the highlights of the second Joint Cold Spring Harbor Laboratory 1SB, UK. and Wellcome Trust ‘Genome Informatics’ Conference, organized by Ewan Birney, E-mail: [email protected] Suzanna Lewis and Lincoln Stein. There were sessions on in silico data discovery, comparative genomics, annotation pipelines, functional genomics and integrative biology. The conference included a keynote address by Sydney Brenner, who was awarded the 2002 Nobel Prize in Physiology or Medicine (jointly with John Sulston and H. Robert Horvitz) a month later. Copyright 2003 John Wiley & Sons, Ltd. In silico data discovery background set was genes which had a log ratio of ∼0 in liver. Their approach found 17 of 17 known In the first of two sessions on this topic, Naoya promoters with a specificity of 17/28. None of the Hata (Cold Spring Harbor Laboratory, USA) sites they identified was located downstream of a spoke about motif searching for tissue specific TSS and all showed an excess in the foreground promoters. The first step in the process is to sample compared to the background sample. -
The Zebrafish Anatomy and Stage Ontologies: Representing the Anatomy and Development of Danio Rerio
Van Slyke et al. Journal of Biomedical Semantics 2014, 5:12 http://www.jbiomedsem.com/content/5/1/12 JOURNAL OF BIOMEDICAL SEMANTICS RESEARCH Open Access The zebrafish anatomy and stage ontologies: representing the anatomy and development of Danio rerio Ceri E Van Slyke1*†, Yvonne M Bradford1*†, Monte Westerfield1,2 and Melissa A Haendel3 Abstract Background: The Zebrafish Anatomy Ontology (ZFA) is an OBO Foundry ontology that is used in conjunction with the Zebrafish Stage Ontology (ZFS) to describe the gross and cellular anatomy and development of the zebrafish, Danio rerio, from single cell zygote to adult. The zebrafish model organism database (ZFIN) uses the ZFA and ZFS to annotate phenotype and gene expression data from the primary literature and from contributed data sets. Results: The ZFA models anatomy and development with a subclass hierarchy, a partonomy, and a developmental hierarchy and with relationships to the ZFS that define the stages during which each anatomical entity exists. The ZFA and ZFS are developed utilizing OBO Foundry principles to ensure orthogonality, accessibility, and interoperability. The ZFA has 2860 classes representing a diversity of anatomical structures from different anatomical systems and from different stages of development. Conclusions: The ZFA describes zebrafish anatomy and development semantically for the purposes of annotating gene expression and anatomical phenotypes. The ontology and the data have been used by other resources to perform cross-species queries of gene expression and phenotype data, providing insights into genetic relationships, morphological evolution, and models of human disease. Background function, development, and evolution. ZFIN, the zebrafish Zebrafish (Danio rerio) share many anatomical and physio- model organism database [10] manually curates these dis- logical characteristics with other vertebrates, including parate data obtained from the literature or by direct data humans, and have emerged as a premiere organism to submission. -
Towards a Knowledge Graph for Science
Towards a Knowledge Graph for Science Invited Article∗ Sören Auer Viktor Kovtun Manuel Prinz TIB Leibniz Information Centre for L3S Research Centre, Leibniz TIB Leibniz Information Centre for Science and Technology and L3S University of Hannover Science and Technology Research Centre at University of Hannover, Germany Hannover, Germany Hannover [email protected] [email protected] Hannover, Germany [email protected] Anna Kasprzik Markus Stocker TIB Leibniz Information Centre for TIB Leibniz Information Centre for Science and Technology Science and Technology Hannover, Germany Hannover, Germany [email protected] [email protected] ABSTRACT KEYWORDS The document-centric workflows in science have reached (or al- Knowledge Graph, Science and Technology, Research Infrastructure, ready exceeded) the limits of adequacy. This is emphasized by Libraries, Information Science recent discussions on the increasing proliferation of scientific lit- ACM Reference Format: erature and the reproducibility crisis. This presents an opportu- Sören Auer, Viktor Kovtun, Manuel Prinz, Anna Kasprzik, and Markus nity to rethink the dominant paradigm of document-centric schol- Stocker. 2018. Towards a Knowledge Graph for Science: Invited Article. arly information communication and transform it into knowledge- In WIMS ’18: 8th International Conference on Web Intelligence, Mining and based information flows by representing and expressing informa- Semantics, June 25–27, 2018, Novi Sad, Serbia. ACM, New York, NY, USA, tion through semantically rich, interlinked knowledge graphs. At 6 pages. https://doi.org/10.1145/3227609.3227689 the core of knowledge-based information flows is the creation and evolution of information models that establish a common under- 1 INTRODUCTION standing of information communicated between stakeholders as The communication of scholarly information is document-centric. -
Biocuration 2016 - Posters
Biocuration 2016 - Posters Source: http://www.sib.swiss/events/biocuration2016/posters 1 RAM: A standards-based database for extracting and analyzing disease-specified concepts from the multitude of biomedical resources Jinmeng Jia and Tieliu Shi Each year, millions of people around world suffer from the consequence of the misdiagnosis and ineffective treatment of various disease, especially those intractable diseases and rare diseases. Integration of various data related to human diseases help us not only for identifying drug targets, connecting genetic variations of phenotypes and understanding molecular pathways relevant to novel treatment, but also for coupling clinical care and biomedical researches. To this end, we built the Rare disease Annotation & Medicine (RAM) standards-based database which can provide reference to map and extract disease-specified information from multitude of biomedical resources such as free text articles in MEDLINE and Electronic Medical Records (EMRs). RAM integrates disease-specified concepts from ICD-9, ICD-10, SNOMED-CT and MeSH (http://www.nlm.nih.gov/mesh/MBrowser.html) extracted from the Unified Medical Language System (UMLS) based on the UMLS Concept Unique Identifiers for each Disease Term. We also integrated phenotypes from OMIM for each disease term, which link underlying mechanisms and clinical observation. Moreover, we used disease-manifestation (D-M) pairs from existing biomedical ontologies as prior knowledge to automatically recognize D-M-specific syntactic patterns from full text articles in MEDLINE. Considering that most of the record-based disease information in public databases are textual format, we extracted disease terms and their related biomedical descriptive phrases from Online Mendelian Inheritance in Man (OMIM), National Organization for Rare Disorders (NORD) and Orphanet using UMLS Thesaurus. -
Human Disease Ontology 2018 Update: Classification, Content And
Published online 8 November 2018 Nucleic Acids Research, 2019, Vol. 47, Database issue D955–D962 doi: 10.1093/nar/gky1032 Human Disease Ontology 2018 update: classification, content and workflow expansion Lynn M. Schriml 1,*, Elvira Mitraka2, James Munro1, Becky Tauber1, Mike Schor1, Lance Nickle1, Victor Felix1, Linda Jeng3, Cynthia Bearer3, Richard Lichenstein3, Katharine Bisordi3, Nicole Campion3, Brooke Hyman3, David Kurland4, Connor Patrick Oates5, Siobhan Kibbey3, Poorna Sreekumar3, Chris Le3, Michelle Giglio1 and Carol Greene3 1University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA, 2Dalhousie University, Halifax, NS, Canada, 3University of Maryland School of Medicine, Baltimore, MD, USA, 4New York University Langone Medical Center, Department of Neurosurgery, New York, NY, USA and 5Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA Received September 13, 2018; Revised October 04, 2018; Editorial Decision October 14, 2018; Accepted October 22, 2018 ABSTRACT INTRODUCTION The Human Disease Ontology (DO) (http://www. The rapid growth of biomedical and clinical research in re- disease-ontology.org), database has undergone sig- cent decades has begun to reveal novel cellular, molecular nificant expansion in the past three years. The DO and environmental determinants of disease (1–4). However, disease classification includes specific formal se- the opportunities for discovery and the transcendence of mantic rules to express meaningful disease mod- knowledge between research groups can only be realized in conjunction with the development of rigorous, standard- els and has expanded from a single asserted clas- ized bioinformatics tools. These tools should be capable of sification to include multiple-inferred mechanistic addressing specific biomedical data nomenclature and stan- disease classifications, thus providing novel per- dardization challenges posed by the vast variety of biomed- spectives on related diseases. -
Gearing up to Handle the Mosaic Nature of Life in the Quest for Orthologs. Kristoffer Forslund
The Jackson Laboratory The Mouseion at the JAXlibrary Faculty Research 2018 Faculty Research 1-15-2018 Gearing up to handle the mosaic nature of life in the quest for orthologs. Kristoffer Forslund Cecile Pereira Salvador Capella-Gutierrez Alan Sousa da Silva Adrian Altenhoff See next page for additional authors Follow this and additional works at: https://mouseion.jax.org/stfb2018 Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons Recommended Citation Forslund, Kristoffer; Pereira, Cecile; Capella-Gutierrez, Salvador; Sousa da Silva, Alan; Altenhoff, Adrian; Huerta-Cepas, Jaime; Muffato, Matthieu; Patricio, Mateus; Vandepoele, Klaas; Ebersberger, Ingo; Blake, Judith A.; Fernández Breis, Jesualdo Tomás; Orthologs Consortium, The Quest for; Boeckmann, Brigitte; Gabaldón, Toni; Sonnhammer, Erik; Dessimoz, Christophe; and Lewis, Suzanna, "Gearing up to handle the mosaic nature of life in the quest for orthologs." (2018). Faculty Research 2018. 25. https://mouseion.jax.org/stfb2018/25 This Article is brought to you for free and open access by the Faculty Research at The ousM eion at the JAXlibrary. It has been accepted for inclusion in Faculty Research 2018 by an authorized administrator of The ousM eion at the JAXlibrary. For more information, please contact [email protected]. Authors Kristoffer Forslund, Cecile Pereira, Salvador Capella-Gutierrez, Alan Sousa da Silva, Adrian Altenhoff, Jaime Huerta-Cepas, Matthieu Muffato, Mateus Patricio, Klaas Vandepoele, Ingo Ebersberger, Judith A. Blake, Jesualdo Tomás -
Challenges for Ontology Repositories and Applications to Biomedicine & Agronomy
Position paper – Keynote SIMBig 2017 – September 2017, Lima, Peru Challenges for ontology repositories and applications to biomedicine & agronomy Clement Jonquet Laboratory of Informatics, Robotics, and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, France & Center for BioMedical Informatics Research (BMIR), Stanford University, USA [email protected] (ORCID: 0000-0002-2404-1582) Abstract 1 Introduction The explosion of the number of ontologies The Semantic Web produces many vocabularies and vocabularies available in the Semantic and ontologies to represent and annotate any kind Web makes ontology libraries and reposi- of data. However, those ontologies are spread out, tories mandatory to find and use them. in different formats, of different size, with differ- Their functionalities span from simple on- ent structures and from overlapping domains. The tology listing with more or less of metada- scientific community has always been interested ta description to portals with advanced on- tology-based services: browse, search, vis- in designing common platforms to list and some- ualization, metrics, annotation, etc. Ontol- time host and serve ontologies, align them, and ogy libraries and repositories are usually enable their (re)use (Ding and Fensel, 2001; developed to address certain needs and Hartmann et al., 2009; D’Aquin and Noy, 2012; , communities. BioPortal, the ontology re- 1995). These platforms range from simple ontol- pository built by the US National Center ogy listings or libraries with structured metadata, for Biomedical Ontologies BioPortal relies to advanced repositories (or portals) which fea- on a domain independent technology al- ture a variety of services for multiple types of ready reused in several projects from bio- semantic resources (ontologies, vocabularies, medicine to agronomy and earth sciences. -
Applications of an Ontology Engineering Methodology Accessing Linked Data for Dialogue-Based Medical Image Retrieval
Applications of an Ontology Engineering Methodology Accessing Linked Data for Dialogue-Based Medical Image Retrieval Daniel Sonntag, Pinar Wennerberg, Sonja Zillner DFKI - German Research Center for AI, Siemens AG [email protected] [email protected] [email protected] Abstract of medication and treatment plan is foreseen. This requires This paper examines first ideas on the applicability of the medical images to be annotated accordingly so that the Linked Data, in particular a subset of the Linked Open Drug radiologists can obtain all the necessary information Data (LODD), to connect radiology, human anatomy, and starting with the image. Moreover, the image information drug information for improved medical image annotation needs to be interlinked in a coherent way to enable the and subsequent search. One outcome of our ontology radiologist to trace the dependencies between observations. engineering methodology is the semi-automatic alignment Linked Data has the potential to provide easier access to between radiology-related OWL ontologies (FMA and significantly growing, publicly available related data sets, RadLex). These can be used to provide new connections in such as those in the healthcare domain: in combination the medicine-related linked data cloud. A use case scenario with ontology matching and speech dialogue (as AI is provided that involves a full-fletched speech dialogue system as AI application and additionally demonstrates the systems) this should allow medical experts to explore the benefits of the approach by enabling the radiologist to query interrelated data more conveniently and efficiently without and explore related data, e.g., medical images and drugs. having to switch between many different applications, data The diagnosis is on a special type of cancer (lymphoma). -
Modularization for the Cell Ontology
Modularization for the Cell Ontology Chris Mungall, Melissa Haendel, Amelia Ireland, Shahid Manzoor, Terry Meehan, David Osumi-Sutherland, Carlo Torniai, Alexander Diehl Outline • The Cell Ontology and its neighbors • Handling multiple species • Extracting modules for taxonomic contexts • The Oort – OBO Ontology Release Tool Inter-ontology axioms Source Ref Axiom cl go ‘mature eosinophil’ SubClassOf capable of some ‘respiratory burst’ go cl ‘eosinophil differentiation’ EquivalentTo differentiation and has_target some eosinophil * cl pro ‘Gr1-high classical monocyte’ EquivalentTo ‘classical monocyte’ and has_plasma_membrane_part some ‘integrin-alpha-M’ and …. cl chebi melanophage EquivalentTo ‘tissue-resident macrophage’ and has_part some melanin and … cl uberon ‘epithelial cell of lung’ EquivalentTo ‘epithelial cell’ and part_of some lung uberon cl muscle tissue EquivalentTo tissue and has_part some muscle cell clo cl HeLa cell SubClassOf derives_from some (‘epithelial cell’ and part_of some cervix) fbbt cl fbbt:neuron EquivalentTo cl:neuron and part_of some Drosophila ** * experimental extension ** taxon-context bridge axiom The cell ontology covers multiple taxonomic contexts Ontology Context # of links to cl MA adult mouse 1 FMA adult human 658 XAO frog 63 AAO amphibian ZFA zebrafish 391 TAO bony fish 385 FBbt fruitfly 53 PO plants * CL all cells - Xref macros: an easy way to formally connect to broader taxa treat-xrefs-as-genus-differentia: CL part_of NCBITaxon:7955! …! [Term]! id: ZFA:0009255! name: amacrine cell! xref: CL:0000561! -
Investigating Term Reuse and Overlap in Biomedical Ontologies Maulik R
Investigating Term Reuse and Overlap in Biomedical Ontologies Maulik R. Kamdar, Tania Tudorache, and Mark A. Musen Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University ABSTRACT CT, to spare the effort in creating already existing quality We investigate the current extent of term reuse and overlap content (e.g., the anatomy taxonomy), to increase their among biomedical ontologies. We use the corpus of biomedical interoperability, and to support its use in electronic health ontologies stored in the BioPortal repository, and analyze three types records (Tudorache et al., 2010). Another benefit of the of reuse constructs: (a) explicit term reuse, (b) xref reuse, and (c) ontology term reuse is that it enables federated search Concept Unique Identifier (CUI) reuse. While there is a term label engines to query multiple, heterogeneous knowledge sources, similarity of approximately 14.4% of the total terms, we observed structured using these ontologies, and eliminates the need for that most ontologies reuse considerably fewer than 5% of their extensive ontology alignment efforts (Kamdar et al., 2014). terms from a concise set of a few core ontologies. We developed For the purpose of this work, we define as term reuse an interactive visualization to explore reuse dependencies among the situation in which the same term is present in two or biomedical ontologies. Moreover, we identified a set of patterns that more ontologies either by direct use of the same identifier, indicate ontology developers did intend to reuse terms from other or via explicit references and mappings. We define as term ontologies, but they were using different and sometimes incorrect overlap the situation in which the term labels in two or more representations. -
Ontology: Tool for Broad Spectrum Knowledge Integration Barry Smith
Foreword to Chinese Translation Ontology: Tool for Broad Spectrum Knowledge Integration Barry Smith BFO: The Beginnings This book was first published in 2015. Its primary target audience was bio- and biomedical informaticians, reflecting the ways in which ontologies had become an established part of the toolset of bio- and biomedical informatics since (roughly) the completion of the Human Genome Project (HGP). As is well known, the success of HGP led to the transformation of biological and clinical sciences into information-driven disciplines and spawned a whole series of new disciplines with names like ‘proteomics’, ‘connectomics’ and ‘toxiocopharmacogenomics’. It was of course not only the human genome that was made available for research but also the genomes of other ‘model organisms’, such as mouse or fly. The remarkable similarities between these genomes and the human genome made it possible to carry out experiments on model organisms and use the results to draw conclusions relevant to our understanding of human health and disease. To bring this about, however, it was necessary to create a controlled vocabulary that could be used for describing salient features of model organisms in a species-neutral way, and to use the terms of this vocabulary to tag the sequence data for all salient organisms. It was with the purpose of creating such a vocabulary that the Gene Ontology (GO) was born in a Montreal hotel bar in 1998.1 Since then the GO has served as mediator between the new genomic data on the one hand, which is accessible only with the aid of computers, and what we might think of as the ‘old biology data’ captured using natural language by means of terms such as ‘cell division’ or ‘mitochondrion’ or ‘protein binding’. -
OLOBO: a New Ontology for Linking and Integrating Open Biological
OLOBO: A new ontology for linking and integrating open biologi- cal and biomedical ontologies Edison Ong1, Yongqun He2,* 1 Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA 2 Unit of Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA ABSTRACT other ontology terms. To integrate all the ontologies, we To link and integrate different Open Biological and Biomedical first identified and collected all ontology terms shared by at Ontologies (OBO) library ontologies, we generated an OLOBO on- tology that uses the Basic Formal Ontology (BFO) as the upper level least two ontologies. This step identified the minimal sets of ontology to align, link, and integrate all existing 9 formal OBO ontology terms shared by different ontologies. To notify Foundry ontologies. Our study identified 524 ontology terms shared from which ontology a term is imported, we generated a by at least two ontologies, which form the core part of OLOBO. Cur- rent OLOBO includes 978 ontology classes, 13 object properties, new annotation property called ‘term from ontology’ and 90 annotation properties. OLOBO was queried, analyzed, and (OLOBO_0000001). Next, we used Ontofox (Xiang et al., applied to identify potential deprecated terms and discrepancy 2010) to identify terms and axioms related to extracted on- among different OBO library ontologies. tology terms. To align with BFO, we manually examined ontologies and ensure appropriate upper level terms added 1 INTRODUCTION for the purpose of ontology linkage and integration. The OBO Foundry aims at establishing a set of ontology development principles (e.g., openness, collaboration) and 2.3 OLOBO source code, deposition, and queries incorporating ontologies following these principles in an The OLOBO source code is openly available at GitHub: evolving non-redundant suite (Smith et al., 2007).