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Documents authored by Ashburner, Michael

Document
DOI: 10.4230/DagSemProc.08131.1
08131 Executive Summary – Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives

Authors: Michael Ashburner, Ulf Leser, and Dietrich Rebholz-Schuhmann

Published in: Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008)

Abstract
Researchers in Text Mining and researchers active in developing ontological resources provide solutions to preserve semantic information properly, i.e. in ontologies and/or fact databases. Researchers from both fields tend to work independently from each other, but there is a shared interest to profit from ongoing research in the complementary domain. The relatedness of both domains has led to the idea to organize a workshop that brings together members of both research domains.
Cite as

Michael Ashburner, Ulf Leser, and Dietrich Rebholz-Schuhmann. 08131 Executive Summary – Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{ashburner_et_al:DagSemProc.08131.1,
  author =	{Ashburner, Michael and Leser, Ulf and Rebholz-Schuhmann, Dietrich},
  title =	{{08131 Executive Summary – Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives}},
  booktitle =	{Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8131},
  editor =	{Michael Ashburner and Ulf Leser and Dietrich Rebholz-Schuhmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.1},
  URN =		{urn:nbn:de:0030-drops-15234},
  doi =		{10.4230/DagSemProc.08131.1},
  annote =	{Keywords: Text Mining, natural language processing, ontologies, ontology design, machine learning, bioinformatics, medical informatics, knowledge management}
}
Document
DOI: 10.4230/DagSemProc.08131.10
NLP and Phenotypes: using Ontologies to link Human Diseases to Animal Models

Authors: N. Washington, M. Gibson, C.J. Mungall, Michael Ashburner, G. Gkoutos, M. Westerfield, M. Haendel, and S. E. Lewis

Published in: Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008)

Abstract
The path to disease gene discovery in humans is often a lengthy one, but can be significantly shortened if links between human and model organism phenotypes are readily available. Collecting and storing these descriptions in a common resource, recorded with ontologies, as well as developing the tools for annotation, access, and analysis are among the goals of the National Center for Biomedical Ontology. The use of well-structured, expert-reviewed ontologies during curation allows biological data to be understandable by both humans and computers, and thereby increases the capacity for meaningful analysis. We have developed the EQ annotation model, which uses ontology terms to label and link together entities, such as anatomical structures, with the qualities describing them. Phenotypes are represented in our model using any combination of entity (such as anatomy) ontologies in combination with an ontology of qualities (PATO). Together with the model organism databases Zfin and FlyBase, we are evaluating this model, using the Phenote Annotation Tool to capture the mutant phenotypes of 200 genes known to cause human disease (from OMIM records) that have corresponding fly and zebrafish mutant phenotypes. The phenotypic data modeled in this way is available from the NCBO Open Biomedical Database (OBD), which has the same underlying annotation data model, and can currently be accessed via a computational (REST) interface for utilization by other external application or databases. This work is funded by the NIH.
Cite as

N. Washington, M. Gibson, C.J. Mungall, Michael Ashburner, G. Gkoutos, M. Westerfield, M. Haendel, and S. E. Lewis. NLP and Phenotypes: using Ontologies to link Human Diseases to Animal Models. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{washington_et_al:DagSemProc.08131.10,
  author =	{Washington, N. and Gibson, M. and Mungall, C.J. and Ashburner, Michael and Gkoutos, G. and Westerfield, M. and Haendel, M. and Lewis, S. E.},
  title =	{{NLP and Phenotypes: using Ontologies to link Human Diseases to Animal Models}},
  booktitle =	{Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8131},
  editor =	{Michael Ashburner and Ulf Leser and Dietrich Rebholz-Schuhmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.10},
  URN =		{urn:nbn:de:0030-drops-15143},
  doi =		{10.4230/DagSemProc.08131.10},
  annote =	{Keywords: Phenotypes, ontologies, annotation}
}
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