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Argudas: lessons for argumentation in biology based on a gene expression use case

BACKGROUND: In situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one su...

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Detalles Bibliográficos
Autores principales: McLeod, Kenneth, Ferguson, Gus, Burger, Albert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471349/
https://www.ncbi.nlm.nih.gov/pubmed/22372999
http://dx.doi.org/10.1186/1471-2105-13-S1-S8
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author McLeod, Kenneth
Ferguson, Gus
Burger, Albert
author_facet McLeod, Kenneth
Ferguson, Gus
Burger, Albert
author_sort McLeod, Kenneth
collection PubMed
description BACKGROUND: In situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one such form of reasoning is computational argumentation. Essentially this involves asking a computer to debate (i.e. reason about) the validity of a particular statement. Arguments are produced for both sides - the statement is true and, the statement is false - then the most powerful argument is used. In this work the computer is asked to debate whether or not a gene is expressed in a particular mouse anatomical structure. The information generated during the debate can be passed to the biological end-user, enabling their own decision-making process. RESULTS: This paper examines the evolution of a system, Argudas, which tests using computational argumentation in an in situ gene hybridisation gene expression use case. Argudas reasons using information extracted from several different online resources that publish gene expression information for the mouse. The development and evaluation of two prototypes is discussed. Throughout a number of issues shall be raised including the appropriateness of computational argumentation in biology and the challenges faced when integrating apparently similar online biological databases. CONCLUSIONS: From the work described in this paper it is clear that for argumentation to be effective in the biological domain the argumentation community need to develop further the tools and resources they provide. Additionally, the biological community must tackle the incongruity between overlapping and adjacent resources, thus facilitating the integration and modelling of biological information. Finally, this work highlights both the importance of, and difficulty in creating, a good model of the domain.
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spelling pubmed-34713492012-10-18 Argudas: lessons for argumentation in biology based on a gene expression use case McLeod, Kenneth Ferguson, Gus Burger, Albert BMC Bioinformatics Research BACKGROUND: In situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one such form of reasoning is computational argumentation. Essentially this involves asking a computer to debate (i.e. reason about) the validity of a particular statement. Arguments are produced for both sides - the statement is true and, the statement is false - then the most powerful argument is used. In this work the computer is asked to debate whether or not a gene is expressed in a particular mouse anatomical structure. The information generated during the debate can be passed to the biological end-user, enabling their own decision-making process. RESULTS: This paper examines the evolution of a system, Argudas, which tests using computational argumentation in an in situ gene hybridisation gene expression use case. Argudas reasons using information extracted from several different online resources that publish gene expression information for the mouse. The development and evaluation of two prototypes is discussed. Throughout a number of issues shall be raised including the appropriateness of computational argumentation in biology and the challenges faced when integrating apparently similar online biological databases. CONCLUSIONS: From the work described in this paper it is clear that for argumentation to be effective in the biological domain the argumentation community need to develop further the tools and resources they provide. Additionally, the biological community must tackle the incongruity between overlapping and adjacent resources, thus facilitating the integration and modelling of biological information. Finally, this work highlights both the importance of, and difficulty in creating, a good model of the domain. BioMed Central 2012-01-25 /pmc/articles/PMC3471349/ /pubmed/22372999 http://dx.doi.org/10.1186/1471-2105-13-S1-S8 Text en Copyright ©2012 McLeod et al. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
McLeod, Kenneth
Ferguson, Gus
Burger, Albert
Argudas: lessons for argumentation in biology based on a gene expression use case
title Argudas: lessons for argumentation in biology based on a gene expression use case
title_full Argudas: lessons for argumentation in biology based on a gene expression use case
title_fullStr Argudas: lessons for argumentation in biology based on a gene expression use case
title_full_unstemmed Argudas: lessons for argumentation in biology based on a gene expression use case
title_short Argudas: lessons for argumentation in biology based on a gene expression use case
title_sort argudas: lessons for argumentation in biology based on a gene expression use case
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471349/
https://www.ncbi.nlm.nih.gov/pubmed/22372999
http://dx.doi.org/10.1186/1471-2105-13-S1-S8
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