Fig. 1 Argumentation flow chart: Steps 2–4 are repeated for each match in the search results, until all have been classified as good, bad or undecided.

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Fig. 1 Argumentation flow chart: Steps 2–4 are repeated for each match in the search results, until all have been classified as good, bad or undecided. From: Capturing expert knowledge with argumentation: a case study in bioinformatics Bioinformatics. 2006;22(8):924-933. doi:10.1093/bioinformatics/btl018 Bioinformatics | © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgThe online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Fig. 2 Model argumentation framework: An example model argumentation framework for 3D-PSSM. The boxes represent individual claims, colour coded to indicate whether they support (green) or oppose (red) a match. Arrows indicate attacks, e.g. ‘Low 3D-PSSM E-value’ (good) attacks ‘Low sequence identity’ (bad). From: Capturing expert knowledge with argumentation: a case study in bioinformatics Bioinformatics. 2006;22(8):924-933. doi:10.1093/bioinformatics/btl018 Bioinformatics | © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgThe online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Fig. 3 Specific argumentation framework: An example argumentation framework for a particular result from 3D-PSSM. Six of the claims from the model framework have been found to apply to the result. Claims which do not apply to the result are blurred: e.g. this result does not have a low sequence identity. Claims with a cross through them are defeated—they are attacked by claims which are not themselves defeated. Four undefeated claims remain, about fold occurrence, number of query homologues, core residues and template length. They are all claims which oppose the result (hence coloured red). Since all undefeated claims agree the result is bad, we conclude that the result is negative—i.e. the found protein structure is not a good model for the query protein sequence. From: Capturing expert knowledge with argumentation: a case study in bioinformatics Bioinformatics. 2006;22(8):924-933. doi:10.1093/bioinformatics/btl018 Bioinformatics | © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgThe online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Fig. 4 Argumentation algorithm: Algorithm for using argumentation to classify results from a search engine for a particular query. From: Capturing expert knowledge with argumentation: a case study in bioinformatics Bioinformatics. 2006;22(8):924-933. doi:10.1093/bioinformatics/btl018 Bioinformatics | © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgThe online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Fig. 5 Decision tree equivalent to argumentation: This tree is equivalent to the argumentation framework in Figure 2. Each question is represented by the symbolic identifier of the equivalent claim, given in Table 1. Follow the left branch when answering ‘yes’ (i.e. the claim applies to the result) and the right branch when answering ‘no’. It can be seen that questions are repeated in different parts of the tree, an inherent problem with decision trees. From: Capturing expert knowledge with argumentation: a case study in bioinformatics Bioinformatics. 2006;22(8):924-933. doi:10.1093/bioinformatics/btl018 Bioinformatics | © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgThe online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org