1
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Kondinski A, Menon A, Nurkowski D, Farazi F, Mosbach S, Akroyd J, Kraft M. Automated Rational Design of Metal-Organic Polyhedra. J Am Chem Soc 2022; 144:11713-11728. [PMID: 35731954 PMCID: PMC9264355 DOI: 10.1021/jacs.2c03402] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations.
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Affiliation(s)
- Aleksandar Kondinski
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Angiras Menon
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Daniel Nurkowski
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Feroz Farazi
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459
- The
Alan Turing Institute, 2QR, John Dodson House, 96 Euston Road, London NW1 2DB, U.K.
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2
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Devanand A, Karmakar G, Krdzavac N, Farazi F, Lim MQ, Foo Eddy Y, Karimi IA, Kraft M. ElChemo: A cross-domain interoperability between chemical and electrical systems in a plant. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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3
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Tiny-UKSIE-An Optimized Lightweight Semantic Inference Engine for Reasoning Uncertain Knowledge. INT J SEMANT WEB INF 2022. [DOI: 10.4018/ijswis.300826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The application of semantic web technologies such as semantic inference to the field of the Internet of Things (IoT) can realize data semantic information enhancement and semantic knowledge discovery, which plays a key role in enhancing data value and application intelligence. However, Mainstream semantic inference engines cannot be applied to IoT computing devices with limited storage resources and weak computing power, and cannot reason about uncertain knowledge. To solve this problem, the authors propose a lightweight semantic inference engine, Tiny-UKSIE, based on the RETE algorithm. The genetic algorithm (GA) is adopted to optimize the Alpha network sequence, and the inference time can be reduced by 8.73% before and after optimization. Moreover, a four-tuple knowledge representation method with probability factors is proposed, and probabilistic inference rules are constructed to enable the inference engine to infer uncertain knowledge. Compared with mainstream inference engines, storage resource usage is reduced by up to 97.37%, and inference time is reduced by up to 24.55%.
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4
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Tena Cucala D, Cuenca Grau B, Horrocks I. Pay-as-you-go consequence-based reasoning for the description logic SROIQ. ARTIF INTELL 2021. [DOI: 10.1016/j.artint.2021.103518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2021; 22:bbaa199. [PMID: 33049044 PMCID: PMC8293838 DOI: 10.1093/bib/bbaa199] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
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Affiliation(s)
| | | | - Xin Gao
- Computational Bioscience Research Center and lead of the Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology
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6
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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7
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Wernhard C. Craig Interpolation with Clausal First-Order Tableaux. J Autom Reason 2021. [DOI: 10.1007/s10817-021-09590-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Naghizadeh A, Salamat M, Hamzeian D, Akbari S, Rezaeizadeh H, Vaghasloo MA, Karbalaei R, Mirzaie M, Karimi M, Jafari M. IrGO: Iranian traditional medicine General Ontology and knowledge base. J Biomed Semantics 2021; 12:9. [PMID: 33863373 PMCID: PMC8052758 DOI: 10.1186/s13326-021-00237-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/04/2021] [Indexed: 11/22/2022] Open
Abstract
Background Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity. Construction and content Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. Utility and discussion The ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica. Conclusion We constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.
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Affiliation(s)
- Ayeh Naghizadeh
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Salamat
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Donya Hamzeian
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Shaghayegh Akbari
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Rezaeizadeh
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Alizadeh Vaghasloo
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modarres University, Jalal Ale Ahmad Highway, Tehran, Iran
| | - Mehrdad Karimi
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohieddin Jafari
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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9
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Bonatti PA, Ioffredo L, Petrova IM, Sauro L, Siahaan IR. Real-time reasoning in OWL2 for GDPR compliance. ARTIF INTELL 2020. [DOI: 10.1016/j.artint.2020.103389] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Schneider T, Šimkus M. Ontologies and Data Management: A Brief Survey. KUNSTLICHE INTELLIGENZ 2020; 34:329-353. [PMID: 32999532 PMCID: PMC7497697 DOI: 10.1007/s13218-020-00686-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 07/22/2020] [Indexed: 11/30/2022]
Abstract
Information systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.
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11
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Lamy JB, Sedki K, Tsopra R. Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments. J Biomed Inform 2020; 104:103407. [DOI: 10.1016/j.jbi.2020.103407] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 11/29/2022]
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12
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13
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Krdzavac N, Mosbach S, Nurkowski D, Buerger P, Akroyd J, Martin J, Menon A, Kraft M. An Ontology and Semantic Web Service for Quantum Chemistry Calculations. J Chem Inf Model 2019; 59:3154-3165. [DOI: 10.1021/acs.jcim.9b00227] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Nenad Krdzavac
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
- CMCL Innovations, Sheraton House, Castle Park, Castle Street, Cambridge, Cambridgeshire CB3 0AX, U.K
| | - Daniel Nurkowski
- CMCL Innovations, Sheraton House, Castle Park, Castle Street, Cambridge, Cambridgeshire CB3 0AX, U.K
| | - Philipp Buerger
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
- CMCL Innovations, Sheraton House, Castle Park, Castle Street, Cambridge, Cambridgeshire CB3 0AX, U.K
| | - Jacob Martin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
| | - Angiras Menon
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge, Cambridgeshire CB3 0AS, U.K
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, Singapore 138602
- CMCL Innovations, Sheraton House, Castle Park, Castle Street, Cambridge, Cambridgeshire CB3 0AX, U.K
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459
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14
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El-Sappagh S, Kwak D, Ali F, Kwak KS. DMTO: a realistic ontology for standard diabetes mellitus treatment. J Biomed Semantics 2018; 9:8. [PMID: 29409535 PMCID: PMC5800094 DOI: 10.1186/s13326-018-0176-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/04/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. RESULTS This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. CONCLUSION The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.
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Affiliation(s)
- Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Informatics, Benha University, Banha Mansura Road, Meit Ghamr - Benha, Banha, Al Qalyubia Governorate 3000-104 Egypt
| | - Daehan Kwak
- Department of Computer Science, Kean University, Union, NJ 07083 USA
| | - Farman Ali
- Department of Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu, Incheon, 22212 South Korea
| | - Kyung-Sup Kwak
- Department of Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu, Incheon, 22212 South Korea
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15
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Lamy JB, Soualmia LF. Formalization of the semantics of iconic languages: An ontology-based method and four semantic-powered applications. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.08.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Gutierrez F, Dou D, de Silva N, Fickas S. Online Reasoning for Semantic Error Detection in Text. JOURNAL ON DATA SEMANTICS 2017. [DOI: 10.1007/s13740-017-0079-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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17
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Lamy JB. Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artif Intell Med 2017; 80:11-28. [DOI: 10.1016/j.artmed.2017.07.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 07/04/2017] [Accepted: 07/06/2017] [Indexed: 10/19/2022]
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18
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19
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Mısırlı G, Hallinan J, Pocock M, Lord P, McLaughlin JA, Sauro H, Wipat A. Data Integration and Mining for Synthetic Biology Design. ACS Synth Biol 2016; 5:1086-1097. [PMID: 27110921 DOI: 10.1021/acssynbio.5b00295] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.
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Affiliation(s)
- Göksel Mısırlı
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | - Jennifer Hallinan
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | - Matthew Pocock
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
- Turing Ate My Hamster Ltd, NE27
0RT Newcastle upon Tyne, United Kingdom
| | - Phillip Lord
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
| | | | - Herbert Sauro
- Department
of Bioengineering, University of Washington, Seattle, Washington 98105, United States
| | - Anil Wipat
- School
of Computing Science, Newcastle University, NE1 7RU Newcastle
upon Tyne, United Kingdom
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20
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Fisher HM, Hoehndorf R, Bazelato BS, Dadras SS, King LE, Gkoutos GV, Sundberg JP, Schofield PN. DermO; an ontology for the description of dermatologic disease. J Biomed Semantics 2016; 7:38. [PMID: 27296450 PMCID: PMC4907256 DOI: 10.1186/s13326-016-0085-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Accepted: 06/03/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There have been repeated initiatives to produce standard nosologies and terminologies for cutaneous disease, some dedicated to the domain and some part of bigger terminologies such as ICD-10. Recently, formally structured terminologies, ontologies, have been widely developed in many areas of biomedical research. Primarily, these address the aim of providing comprehensive working terminologies for domains of knowledge, but because of the knowledge contained in the relationships between terms they can also be used computationally for many purposes. RESULTS We have developed an ontology of cutaneous disease, constructed manually by domain experts. With more than 3000 terms, DermO represents the most comprehensive formal dermatological disease terminology available. The disease entities are categorized in 20 upper level terms, which use a variety of features such as anatomical location, heritability, affected cell or tissue type, or etiology, as the features for classification, in line with professional practice and nosology in dermatology. Available in OBO flatfile and OWL 2 formats, it is integrated semantically with other ontologies and terminologies describing diseases and phenotypes. We demonstrate the application of DermO to text mining the biomedical literature and in the creation of a network describing the phenotypic relationships between cutaneous diseases. CONCLUSIONS DermO is an ontology with broad coverage of the domain of dermatologic disease and we demonstrate here its utility for text mining and investigation of phenotypic relationships between dermatologic disorders. We envision that in the future it may be applied to the creation and mining of electronic health records, clinical training and basic research, as it supports automated inference and reasoning, and for the broader integration of skin disease information with that from other domains.
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Affiliation(s)
- Hannah M Fisher
- Dept. of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Kingdom of Saudi Arabia
| | - Bruno S Bazelato
- Dept. of Computer Science, Llandinam Building, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK
| | - Soheil S Dadras
- Dept. Dermatology and Pathology, University of Connecticut Health Center, 263, Farmington Avenue, Farmington, CT, 06030, USA
| | - Lloyd E King
- Dept. of Medicine, Div. Dermatology, Vanderbilt University, Nashville, Tennessee, USA
| | - Georgios V Gkoutos
- Dept. of Computer Science, Llandinam Building, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK.,College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2TT, UK
| | - John P Sundberg
- The Jackson Laboratory, 600, Main Street, Bar Harbor Maine, ME 04609-1500, USA
| | - Paul N Schofield
- Dept. of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK. .,The Jackson Laboratory, 600, Main Street, Bar Harbor Maine, ME 04609-1500, USA.
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23
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FoodWiki: a Mobile App Examines Side Effects of Food Additives Via Semantic Web. J Med Syst 2015; 40:41. [PMID: 26590979 DOI: 10.1007/s10916-015-0372-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 10/09/2015] [Indexed: 10/22/2022]
Abstract
In this article, a research project on mobile safe food consumption system (FoodWiki) is discussed that performs its own inferencing rules in its own knowledge base. Currently, the developed rules examines the side effects that are causing some health risks: heart disease, diabetes, allergy, and asthma as initial. There are thousands compounds added to the processed food by food producers with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emulsify, and so forth. Those commonly used ingredients or compounds in manufactured foods may have many side effects that cause several health risks such as heart disease, hypertension, cholesterol, asthma, diabetes, allergies, alzheimer etc. according to World Health Organization. Safety in food consumption, especially by patients in these risk groups, has become crucial, given that such health problems are ranked in the top ten health risks around the world. It is needed personal e-health knowledge base systems to help patients take control of their safe food consumption. The systems with advanced semantic knowledge base can provide recommendations of appropriate foods before consumption by individuals. The proposed FoodWiki system is using a concept based search mechanism that performs on thousands food compounds to provide more relevant information.
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Classifying and querying very large taxonomies with bit-vector encoding. J Intell Inf Syst 2015. [DOI: 10.1007/s10844-015-0383-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Specification and automated design-time analysis of the business process human resource perspective. INFORM SYST 2015. [DOI: 10.1016/j.is.2015.03.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Samwald M, Miñarro Giménez JA, Boyce RD, Freimuth RR, Adlassnig KP, Dumontier M. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies. BMC Med Inform Decis Mak 2015; 15:12. [PMID: 25880555 PMCID: PMC4340468 DOI: 10.1186/s12911-015-0130-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Accepted: 01/13/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. METHODS We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. RESULTS Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. CONCLUSIONS The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.
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Affiliation(s)
- Matthias Samwald
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Jose Antonio Miñarro Giménez
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.,Institute of Medical Informatics, Statistics, and Documentation; Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Suite 419, Pittsburgh, PA, 15206-3701, USA
| | - Robert R Freimuth
- Department of Health Sciences Research; Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Klaus-Peter Adlassnig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.,Medexter Healthcare GmbH, Borschkegasse 7/5, 1090, Vienna, Austria
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5479, USA
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Hoehndorf R, Slater L, Schofield PN, Gkoutos GV. Aber-OWL: a framework for ontology-based data access in biology. BMC Bioinformatics 2015; 16:26. [PMID: 25627673 PMCID: PMC4384359 DOI: 10.1186/s12859-015-0456-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 01/09/2015] [Indexed: 11/10/2022] Open
Abstract
Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. Results We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net. Conclusions Aber-OWL provides a framework for automatically accessing information that is annotated with ontologies or contains terms used to label classes in ontologies. When using Aber-OWL, access to ontologies and data annotated with them is not merely based on class names or identifiers but rather on the knowledge the ontologies contain and the inferences that can be drawn from it.
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Affiliation(s)
- Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. .,Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia.
| | - Luke Slater
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. .,Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. .,Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB, UK.
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK.
| | - Georgios V Gkoutos
- Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB, UK.
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Borgwardt S, Distel F, Peñaloza R. The limits of decidability in fuzzy description logics with general concept inclusions. ARTIF INTELL 2015. [DOI: 10.1016/j.artint.2014.09.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ongenae F, Famaey J, Verstichel S, De Zutter S, Latré S, Ackaert A, Verhoeve P, De Turck F. Ambient-aware continuous care through semantic context dissemination. BMC Med Inform Decis Mak 2014; 14:97. [PMID: 25476007 PMCID: PMC4320491 DOI: 10.1186/1472-6947-14-97] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Accepted: 06/19/2014] [Indexed: 11/10/2022] Open
Abstract
Background The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.
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Affiliation(s)
- Femke Ongenae
- Information Technology Department (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8, 9050 Ghent, Belgium.
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Distributed reasoning with coupled ontologies: the $$E\text {-}{\mathcal {SHIQ}}$$ E - SHIQ representation framework. Knowl Inf Syst 2014. [DOI: 10.1007/s10115-014-0807-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Abstract
The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.
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Affiliation(s)
| | | | | | - Manfred Hauswirth
- Technical University of Berlin, Berlin, Germany & Fraunhofer FOKUS, Berlin, Germany
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Liang SF, Taweel A, Miles S, Kovalchuk Y, Spiridou A, Barratt B, Hoang U, Crichton S, Delaney BC, Wolfe C. Semi automated transformation to OWL formatted files as an approach to data integration. A feasibility study using environmental, disease register and primary care clinical data. Methods Inf Med 2014; 54:32-40. [PMID: 24903775 DOI: 10.3414/me13-02-0029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Accepted: 04/23/2014] [Indexed: 11/09/2022]
Abstract
INTRODUCTION This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND Data heterogeneity is one of the critical problems in analysing, reusing, sharing or linking datasets. Metadata, whilst adding semantic description to data, adds an additional layer of complexity in the heterogeneity of metadata descriptors themselves. This can be managed by using a pre-defined model to extract the metadata, but this can reduce the richness of the data extracted. OBJECTIVES to link the South London Stroke Register (SLSR), the London Air Pollution toolkit (LAP) and the Clinical Practice Research Datalink (CPRD) while transforming data into the Web Ontology Language (OWL) format. METHODS We used a four-step transformation approach to prepare meta-descriptions, convert data, generate and update meta-classes and generate OWL files. We validated the correctness of the transformed OWL files by issuing queries and assessing results against the original source data. RESULTS We have transformed SLSR LAP and CPRD into OWL format. The linked SLSR and CPRD OWL file contains 3644 male and 3551 female patients. The linked SLSR and LAP OWL file shows that there are 17 out of 35 outward postcode areas, where no overlapping data can support further analysis between SLSR and LAP. CONCLUSIONS Our approach generated a resultant set of transformed OWL formatted files, which are in a query-able format to run individual queries, or can be easily converted into other more suitable formats for further analysis, and the transformation was faithful with no loss or anomalies. Our results have shown that the proposed method provides a promising general approach to address data heterogeneity.
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Affiliation(s)
- S F Liang
- Shao Fen Liang, 7th Floor, Capital House, 42 Weston Street, London, SE1 3QD, United Kingdom, E-mail:
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Antunes G, Bakhshandeh M, Mayer R, Borbinha J, Caetano A. Using Ontologies for Enterprise Architecture Integration and Analysis. COMPLEX SYSTEMS INFORMATICS AND MODELING QUARTERLY 2014. [DOI: 10.7250/csimq.2014-1.01] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Combining Fuzzy Ontology Reasoning and Mamdani Fuzzy Inference System with HyFOM Reasoner. ENTERP INF SYST-UK 2014. [DOI: 10.1007/978-3-319-09492-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Scalmato A, Sgorbissa A, Zaccaria R. Describing and recognizing patterns of events in smart environments with description logic. IEEE TRANSACTIONS ON CYBERNETICS 2013; 43:1882-1897. [PMID: 23757579 DOI: 10.1109/tsmcb.2012.2234739] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper describes a system for context awareness in smart environments, which is based on an ontology expressed in description logic and implemented in OWL 2 EL, which is a subset of the Web Ontology Language that allows for reasoning in polynomial time. The approach is different from all other works in the literature since the proposed system requires only the basic reasoning mechanisms of description logic, i.e., subsumption and instance checking, without any additional external reasoning engine. Experiments performed with data collected in three different scenarios are described, i.e., the CASAS Project at Washington State University, the assisted living facility Villa Basilea in Genoa, and the Merry Porter mobile robot at the Polyclinic of Modena.
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Seltmann S, Stachelscheid H, Damaschun A, Jansen L, Lekschas F, Fontaine JF, Nguyen-Dobinsky TN, Leser U, Kurtz A. CELDA -- an ontology for the comprehensive representation of cells in complex systems. BMC Bioinformatics 2013; 14:228. [PMID: 23865855 PMCID: PMC3722091 DOI: 10.1186/1471-2105-14-228] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 07/15/2013] [Indexed: 01/29/2023] Open
Abstract
Background The need for detailed description and modeling of cells drives the continuous generation of large and diverse datasets. Unfortunately, there exists no systematic and comprehensive way to organize these datasets and their information. CELDA (Cell: Expression, Localization, Development, Anatomy) is a novel ontology for the association of primary experimental data and derived knowledge to various types of cells of organisms. Results CELDA is a structure that can help to categorize cell types based on species, anatomical localization, subcellular structures, developmental stages and origin. It targets cells in vitro as well as in vivo. Instead of developing a novel ontology from scratch, we carefully designed CELDA in such a way that existing ontologies were integrated as much as possible, and only minimal extensions were performed to cover those classes and areas not present in any existing model. Currently, ten existing ontologies and models are linked to CELDA through the top-level ontology BioTop. Together with 15.439 newly created classes, CELDA contains more than 196.000 classes and 233.670 relationship axioms. CELDA is primarily used as a representational framework for modeling, analyzing and comparing cells within and across species in CellFinder, a web based data repository on cells (http://cellfinder.org). Conclusions CELDA can semantically link diverse types of information about cell types. It has been integrated within the research platform CellFinder, where it exemplarily relates cell types from liver and kidney during development on the one hand and anatomical locations in humans on the other, integrating information on all spatial and temporal stages. CELDA is available from the CellFinder website: http://cellfinder.org/about/ontology.
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Affiliation(s)
- Stefanie Seltmann
- Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
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Colineau N, Paris C, Vander Linden K. Automatically producing tailored web materials for public administration. NEW REV HYPERMEDIA M 2013. [DOI: 10.1080/13614568.2013.809155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Lamy JB, Soualmia LF, Kerdelhué G, Venot A, Duclos C. Validating the semantics of a medical iconic language using ontological reasoning. J Biomed Inform 2013; 46:56-67. [PMID: 22975315 DOI: 10.1016/j.jbi.2012.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 07/19/2012] [Accepted: 08/24/2012] [Indexed: 11/15/2022]
Affiliation(s)
- Jean-Baptiste Lamy
- LIM&BIO (EA3969), UFR SMBH, University Paris 13, Sorbonne Paris Cité, Bobigny, France.
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Ruiz-Martínez JM, Valencia-García R, Martínez-Béjar R, Hoffmann A. BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts. Knowl Based Syst 2012. [DOI: 10.1016/j.knosys.2012.06.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ganzinger M, He S, Breuhahn K, Knaup P. On the ontology based representation of cell lines. PLoS One 2012; 7:e48584. [PMID: 23144907 PMCID: PMC3492450 DOI: 10.1371/journal.pone.0048584] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Accepted: 09/26/2012] [Indexed: 11/23/2022] Open
Abstract
Cell lines are frequently used as highly standardized and reproducible in vitro models for biomedical analyses and assays. Cell lines are distributed by cell banks that operate databases describing their products. However, the description of the cell lines' properties are not standardized across different cell banks. Existing cell line-related ontologies mostly focus on the description of the cell lines' names, but do not cover aspects like the origin or optimal growth conditions. The objective of this work is to develop an ontology that allows for a more comprehensive description of cell lines and their metadata, which should cover the data elements provided by cell banks. This will provide the basis for the standardized annotation of cell lines and corresponding assays in biomedical research. In addition, the ontology will be the foundation for automated evaluation of such assays and their respective protocols in the future. To accomplish this, a broad range of cell bank databases as well as existing ontologies were analyzed in a comprehensive manner. We identified existing ontologies capable of covering different aspects of the cell line domain. However, not all data fields derived from the cell banks' databases could be mapped to existing ontologies. As a result, we created a new ontology called cell culture ontology (CCONT) integrating existing ontologies where possible. CCONT provides classes from the areas of cell line identification, origin, cell line properties, propagation and tests performed.
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Affiliation(s)
- Matthias Ganzinger
- Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany.
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Abstract
In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities (e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc.). Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO (Suggested Upper Merged Ontology). Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors’ study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.
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Affiliation(s)
- Javier Àlvez
- Department of Languages and Information Systems, University of the Basque Country, Donostia, Gipuzkoa, Spain
| | - Paqui Lucio
- Department of Languages and Information Systems, University of the Basque Country, Donostia, Gipuzkoa, Spain
| | - German Rigau
- Department of Languages and Information Systems, University of the Basque Country, Donostia, Gipuzkoa, Spain
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Gkoutos GV, Schofield PN, Hoehndorf R. Computational tools for comparative phenomics: the role and promise of ontologies. Mamm Genome 2012; 23:669-79. [PMID: 22814867 DOI: 10.1007/s00335-012-9404-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2012] [Accepted: 05/21/2012] [Indexed: 11/28/2022]
Abstract
A major aim of the biological sciences is to gain an understanding of human physiology and disease. One important step towards such a goal is the discovery of the function of genes that will lead to a better understanding of the physiology and pathophysiology of organisms, which will ultimately lead to better diagnosis and therapy. Our increasing ability to phenotypically characterise genetic variants of model organisms coupled with systematic and hypothesis-driven mutagenesis is resulting in a wealth of information that could potentially provide insight into the functions of all genes in an organism. The challenge we are now facing is to develop computational methods that can integrate and analyse such data. The introduction of formal ontologies that make their semantics explicit and accessible to automated reasoning provides the tantalizing possibility of standardizing biomedical knowledge allowing for novel, powerful queries that bridge multiple domains, disciplines, species, and levels of granularity. We review recent computational approaches that facilitate the integration of experimental data from model organisms with clinical observations in humans. These methods foster novel cross-species analysis approaches, thereby enabling comparative phenomics and leading to the potential of translating basic discoveries from the model systems into diagnostic and therapeutic advances at the clinical level.
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Affiliation(s)
- Georgios V Gkoutos
- Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK.
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Shchekotykhin K, Friedrich G, Fleiss P, Rodler P. Interactive ontology debugging: Two query strategies for efficient fault localization. WEB SEMANTICS (ONLINE) 2012; 12-13:88-103. [PMID: 23543507 PMCID: PMC3611094 DOI: 10.1016/j.websem.2011.12.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple "split-in-half" strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the "split-in-half" approach. We experimented with different probability distributions of user errors and different qualities of the a priori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.
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Hoehndorf R, Dumontier M, Oellrich A, Rebholz-Schuhmann D, Schofield PN, Gkoutos GV. Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning. PLoS One 2011; 6:e22006. [PMID: 21789201 PMCID: PMC3138764 DOI: 10.1371/journal.pone.0022006] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2011] [Accepted: 06/12/2011] [Indexed: 11/18/2022] Open
Abstract
Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.
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Affiliation(s)
- Robert Hoehndorf
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Michel Dumontier
- Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, Ottawa, Ontario, Canada
| | - Anika Oellrich
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | | | - Paul N. Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
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