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Journal articles on the topic 'Bioinformatics Ontology'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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1

Giudicelli, V., and M. P. Lefranc. "Ontology for immunogenetics: the IMGT-ONTOLOGY." Bioinformatics 15, no. 12 (1999): 1047–54. http://dx.doi.org/10.1093/bioinformatics/15.12.1047.

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2

Moore, B., G. Fan, and K. Eilbeck. "SOBA: sequence ontology bioinformatics analysis." Nucleic Acids Research 38, Web Server (2010): W161—W164. http://dx.doi.org/10.1093/nar/gkq426.

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3

Baker, P. G., C. A. Goble, S. Bechhofer, N. W. Paton, R. Stevens, and A. Brass. "An ontology for bioinformatics applications." Bioinformatics 15, no. 6 (1999): 510–20. http://dx.doi.org/10.1093/bioinformatics/15.6.510.

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4

Stevens, R. "Ontology-based knowledge representation for bioinformatics." Briefings in Bioinformatics 1, no. 4 (2000): 398–414. http://dx.doi.org/10.1093/bib/1.4.398.

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5

Stevens, Robert. "Ontology Based Document Enrichment in Bioinformatics." Comparative and Functional Genomics 3, no. 1 (2002): 42–46. http://dx.doi.org/10.1002/cfg.141.

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Controlled vocabularies are common within bioinformatics resources. They can be used to give a summary of the knowledge held about a particular entity. They are also used to constrain values given for particular attributes of an entity. This helps create a shared understanding of a domain and aids increased precision and recall during querying of resources. Ontologies can also provide such facilities, but can also enhance their utility. Controlled vocabularies are often simply lists of words, but may be viewed as a kind of ontology. Ideally ontologies are structurally enriched with relationships between terms within the vocabulary. Use of such rich forms of vocabularies in database annotation could enhance those resources usability by both humans and computers. The representation of the knowledge content of biological resources in a computationally accessible form opens the prospect of greater support for a biologist investigating new data.
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6

Viti, Federica, Ivan Merelli, Andrea Calabria, et al. "Ontology-based resources for bioinformatics analysis." International Journal of Metadata, Semantics and Ontologies 6, no. 1 (2011): 35. http://dx.doi.org/10.1504/ijmso.2011.042488.

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7

Stevens, R., C. Goble, I. Horrocks, and S. Bechhofer. "Building a bioinformatics ontology using OIL." IEEE Transactions on Information Technology in Biomedicine 6, no. 2 (2002): 135–41. http://dx.doi.org/10.1109/titb.2002.1006301.

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8

Du, P., G. Feng, J. Flatow, et al. "From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations." Bioinformatics 25, no. 12 (2009): i63—i68. http://dx.doi.org/10.1093/bioinformatics/btp193.

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9

Shuang Qiu, Yadong Wang, Liran Juan, Mingxiang Teng, and Liang Cheng. "Bioinformatics Database Integration Based on Biomedical Ontology." International Journal of Advancements in Computing Technology 3, no. 2 (2011): 66–75. http://dx.doi.org/10.4156/ijact.vol3.issue2.9.

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10

Digiampietri, Luciano A., Jose de J. Perez Alcazar, and Claudia Bauzer Medeiros. "An ontology-based framework for bioinformatics workflows." International Journal of Bioinformatics Research and Applications 3, no. 3 (2007): 268. http://dx.doi.org/10.1504/ijbra.2007.015003.

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11

Lambrix, P., M. Habbouche, and M. Perez. "Evaluation of ontology development tools for bioinformatics." Bioinformatics 19, no. 12 (2003): 1564–71. http://dx.doi.org/10.1093/bioinformatics/btg194.

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12

Joslyn, C. A., S. M. Mniszewski, A. Fulmer, and G. Heaton. "The Gene Ontology Categorizer." Bioinformatics 20, Suppl 1 (2004): i169—i177. http://dx.doi.org/10.1093/bioinformatics/bth921.

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13

Wolstencroft, K., P. Lord, L. Tabernero, A. Brass, and R. Stevens. "Protein classification using ontology classification." Bioinformatics 22, no. 14 (2006): e530-e538. http://dx.doi.org/10.1093/bioinformatics/btl208.

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14

Schulz, S., H. Stenzhorn, and M. Boeker. "The ontology of biological taxa." Bioinformatics 24, no. 13 (2008): i313—i321. http://dx.doi.org/10.1093/bioinformatics/btn158.

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15

Aitken, S., R. Korf, B. Webber, and J. Bard. "COBrA: a bio-ontology editor." Bioinformatics 21, no. 6 (2004): 825–26. http://dx.doi.org/10.1093/bioinformatics/bti097.

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16

Diehl, A. D., J. A. Lee, R. H. Scheuermann, and J. A. Blake. "Ontology development for biological systems: immunology." Bioinformatics 23, no. 7 (2007): 913–15. http://dx.doi.org/10.1093/bioinformatics/btm029.

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17

Soldatova, L. N., A. Clare, A. Sparkes, and R. D. King. "An ontology for a Robot Scientist." Bioinformatics 22, no. 14 (2006): e464-e471. http://dx.doi.org/10.1093/bioinformatics/btl207.

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18

Lee, B., K. Brown, Y. Hathout, and J. Seo. "GOTreePlus: an interactive gene ontology browser." Bioinformatics 24, no. 7 (2008): 1026–28. http://dx.doi.org/10.1093/bioinformatics/btn068.

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19

Wolstencroft, K., S. Owen, M. Horridge, et al. "RightField: embedding ontology annotation in spreadsheets." Bioinformatics 27, no. 14 (2011): 2021–22. http://dx.doi.org/10.1093/bioinformatics/btr312.

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20

Adams, N., R. Hoehndorf, G. V. Gkoutos, G. Hansen, and C. Hennig. "PIDO: the primary immunodeficiency disease ontology." Bioinformatics 27, no. 22 (2011): 3193–99. http://dx.doi.org/10.1093/bioinformatics/btr531.

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21

Harris, M. A., A. Lock, J. Bahler, S. G. Oliver, and V. Wood. "FYPO: the fission yeast phenotype ontology." Bioinformatics 29, no. 13 (2013): 1671–78. http://dx.doi.org/10.1093/bioinformatics/btt266.

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22

Wolstencroft, K., R. McEntire, R. Stevens, L. Tabernero, and A. Brass. "Constructing ontology-driven protein family databases." Bioinformatics 21, no. 8 (2004): 1685–92. http://dx.doi.org/10.1093/bioinformatics/bti158.

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23

Smaili, Fatima Zohra, Xin Gao, and Robert Hoehndorf. "Formal axioms in biomedical ontologies improve analysis and interpretation of associated data." Bioinformatics 36, no. 7 (2019): 2229–36. http://dx.doi.org/10.1093/bioinformatics/btz920.

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Abstract Motivation Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling. Results We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein–protein interactions and gene–disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. Availability and implementation https://github.com/bio-ontology-research-group/tsoe. Supplementary information Supplementary data are available at Bioinformatics online.
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24

Day-Richter, J., M. A. Harris, M. Haendel, and S. Lewis. "OBO-Edit an ontology editor for biologists." Bioinformatics 23, no. 16 (2007): 2198–200. http://dx.doi.org/10.1093/bioinformatics/btm112.

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25

Groß, Anika, Michael Hartung, Kay Prüfer, Janet Kelso, and Erhard Rahm. "Impact of ontology evolution on functional analyses." Bioinformatics 28, no. 20 (2012): 2671–77. http://dx.doi.org/10.1093/bioinformatics/bts498.

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26

Wächter, Thomas, and Michael Schroeder. "Semi-automated ontology generation within OBO-Edit." Bioinformatics 26, no. 12 (2010): i88—i96. http://dx.doi.org/10.1093/bioinformatics/btq188.

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27

Pasquier, C., F. Girardot, K. Jevardat de Fombelle, and R. Christen. "THEA: ontology-driven analysis of microarray data." Bioinformatics 20, no. 16 (2004): 2636–43. http://dx.doi.org/10.1093/bioinformatics/bth295.

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28

Westbrook, J. D., and P. E. Bourne. "STAR/mmCIF: An ontology for macromolecular structure." Bioinformatics 16, no. 2 (2000): 159–68. http://dx.doi.org/10.1093/bioinformatics/16.2.159.

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29

Peng, Jiajie, Tao Wang, Jixuan Wang, Yadong Wang, and Jin Chen. "Extending gene ontology with gene association networks." Bioinformatics 32, no. 8 (2015): 1185–94. http://dx.doi.org/10.1093/bioinformatics/btv712.

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30

Mlecnik, Bernhard, Jérôme Galon, and Gabriela Bindea. "Automated exploration of gene ontology term and pathway networks with ClueGO-REST." Bioinformatics 35, no. 19 (2019): 3864–66. http://dx.doi.org/10.1093/bioinformatics/btz163.

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Abstract Summary Large scale technologies produce massive amounts of experimental data that need to be investigated. To improve their biological interpretation we have developed ClueGO, a Cytoscape App that selects representative Gene Onology terms and pathways for one or multiple lists of genes/proteins and visualizes them into functionally organized networks. Because of its reliability, userfriendliness and support of many species ClueGO gained a large community of users. To further allow scientists programmatic access to ClueGO with R, Python, JavaScript etc., we implemented the cyREST API into ClueGO. In this article we describe this novel, complementary way of accessing ClueGO via REST, and provide R and Phyton examples to demonstrate how ClueGO workflows can be integrated into bioinformatic analysis pipelines. Availability and implementation ClueGO is available in the Cytoscape App Store (http://apps.cytoscape.org/apps/cluego). Supplementary information Supplementary data are available at Bioinformatics online.
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31

Warrender, Jennifer D., Anthony V. Moorman, and Phillip Lord. "A fully computational and reasonable representation for karyotypes." Bioinformatics 35, no. 24 (2019): 5264–70. http://dx.doi.org/10.1093/bioinformatics/btz440.

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Abstract Summary The human karyotype has been used as a mechanism for describing and detecting gross abnormalities in the genome for many decades. It is used both for routine diagnostic purposes and for research to further our understanding of the causes of disease. Despite these important applications there has been no rigorous computational representation of the karyotype; rather an informal, string-based representation is used, making it hard to check, organize and search data of this form. In this article, we describe our use of OWL, the Ontology Web Language, to generate a fully computational representation of the karyotype; the development of this ontology represents a significant advance from the traditional bioinformatics use for tagging and navigation and has necessitated the development of a new ontology development environment called Tawny-OWL. Availability and implementation The Karyotype Ontology and associated Tawny-OWL source code is available on GitHub at https://github.com/jaydchan/tawny-karyotype, under a LGPL License, Version 3.0.
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32

Wolstencroft, K., P. Alper, D. Hull, et al. "The myGrid ontology: bioinformatics service discovery." International Journal of Bioinformatics Research and Applications 3, no. 3 (2007): 303. http://dx.doi.org/10.1504/ijbra.2007.015005.

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33

Schuurman, Nadine, and Agnieszka Leszczynski. "Ontologies for Bioinformatics." Bioinformatics and Biology Insights 2 (January 2008): BBI.S451. http://dx.doi.org/10.4137/bbi.s451.

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The past twenty years have witnessed an explosion of biological data in diverse database formats governed by heterogeneous infrastructures. Not only are semantics (attribute terms) different in meaning across databases, but their organization varies widely. Ontologies are a concept imported from computing science to describe different conceptual frameworks that guide the collection, organization and publication of biological data. An ontology is similar to a paradigm but has very strict implications for formatting and meaning in a computational context. The use of ontologies is a means of communicating and resolving semantic and organizational differences between biological databases in order to enhance their integration. The purpose of interoperability (or sharing between divergent storage and semantic protocols) is to allow scientists from around the world to share and communicate with each other. This paper describes the rapid accumulation of biological data, its various organizational structures, and the role that ontologies play in interoperability.
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34

Myhre, S., H. Tveit, T. Mollestad, and A. Laegreid. "Additional Gene Ontology structure for improved biological reasoning." Bioinformatics 22, no. 16 (2006): 2020–27. http://dx.doi.org/10.1093/bioinformatics/btl334.

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35

Friedman, C., T. Borlawsky, L. Shagina, H. R. Xing, and Y. A. Lussier. "Bio-Ontology and text: bridging the modeling gap." Bioinformatics 22, no. 19 (2006): 2421–29. http://dx.doi.org/10.1093/bioinformatics/btl405.

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36

Carbon, Seth, Amelia Ireland, Christopher J. Mungall, ShengQiang Shu, Brad Marshall, and Suzanna Lewis. "AmiGO: online access to ontology and annotation data." Bioinformatics 25, no. 2 (2008): 288–89. http://dx.doi.org/10.1093/bioinformatics/btn615.

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37

Verspoor, K., D. Dvorkin, K. B. Cohen, and L. Hunter. "Ontology quality assurance through analysis of term transformations." Bioinformatics 25, no. 12 (2009): i77—i84. http://dx.doi.org/10.1093/bioinformatics/btp195.

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38

Kulmanov, Maxat, Paul N. Schofield, Georgios V. Gkoutos, and Robert Hoehndorf. "Ontology-based validation and identification of regulatory phenotypes." Bioinformatics 34, no. 17 (2018): i857—i865. http://dx.doi.org/10.1093/bioinformatics/bty605.

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39

Hartung, M., A. Gross, and E. Rahm. "CODEX: exploration of semantic changes between ontology versions." Bioinformatics 28, no. 6 (2012): 895–96. http://dx.doi.org/10.1093/bioinformatics/bts029.

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40

Pirooznia, Mehdi, Tao Wang, Dimitrios Avramopoulos, et al. "SynaptomeDB: an ontology-based knowledgebase for synaptic genes." Bioinformatics 28, no. 6 (2012): 897–99. http://dx.doi.org/10.1093/bioinformatics/bts040.

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41

Zhang, Chao, Jiguang Wang, Kristina Hanspers, Dong Xu, Luonan Chen, and Alexander R. Pico. "NOA: a cytoscape plugin for network ontology analysis." Bioinformatics 29, no. 16 (2013): 2066–67. http://dx.doi.org/10.1093/bioinformatics/btt334.

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42

Malone, James, Ele Holloway, Tomasz Adamusiak, et al. "Modeling sample variables with an Experimental Factor Ontology." Bioinformatics 26, no. 8 (2010): 1112–18. http://dx.doi.org/10.1093/bioinformatics/btq099.

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43

Warwick Vesztrocy, Alex, and Christophe Dessimoz. "Benchmarking gene ontology function predictions using negative annotations." Bioinformatics 36, Supplement_1 (2020): i210—i218. http://dx.doi.org/10.1093/bioinformatics/btaa466.

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Abstract Motivation With the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations. Results This article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments. Availability and Implementation All data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not. Supplementary information Supplementary data are available at Bioinformatics online.
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44

Kohler, J., S. Philippi, and M. Lange. "SEMEDA: ontology based semantic integration of biological databases." Bioinformatics 19, no. 18 (2003): 2420–27. http://dx.doi.org/10.1093/bioinformatics/btg340.

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45

Adryan, B., and R. Schuh. "Gene-Ontology-based clustering of gene expression data." Bioinformatics 20, no. 16 (2004): 2851–52. http://dx.doi.org/10.1093/bioinformatics/bth289.

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46

Shoop, E., P. Casaes, G. Onsongo, et al. "Data exploration tools for the Gene Ontology database." Bioinformatics 20, no. 18 (2004): 3442–54. http://dx.doi.org/10.1093/bioinformatics/bth425.

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47

Gligorijević, Vladimir, Vuk Janjić, and Nataša Pržulj. "Integration of molecular network data reconstructs Gene Ontology." Bioinformatics 30, no. 17 (2014): i594—i600. http://dx.doi.org/10.1093/bioinformatics/btu470.

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48

Swat, Maciej J., Pierre Grenon, and Sarala Wimalaratne. "ProbOnto: ontology and knowledge base of probability distributions." Bioinformatics 32, no. 17 (2016): 2719–21. http://dx.doi.org/10.1093/bioinformatics/btw170.

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49

Fu, Guangyuan, Jun Wang, Bo Yang, and Guoxian Yu. "NegGOA: negative GO annotations selection using ontology structure." Bioinformatics 32, no. 19 (2016): 2996–3004. http://dx.doi.org/10.1093/bioinformatics/btw366.

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50

Yu, U., Y. J. Choi, J. K. Choi, and S. Kim. "TO-GO: a Java-based Gene Ontology navigation environment." Bioinformatics 21, no. 17 (2005): 3580–81. http://dx.doi.org/10.1093/bioinformatics/bti560.

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