Capturing expert knowledge with argumentation: a case study in bioinformatics

  • Authors:
  • Benjamin R. Jefferys;Lawrence A. Kelley;Marek J. Sergot;John Fox;Michael J. E. Sternberg

  • Affiliations:
  • Biochemistry Building, Division of Molecular Biosciences, Imperial College London SW7 2AZ, UK;Biochemistry Building, Division of Molecular Biosciences, Imperial College London SW7 2AZ, UK;Department of Computing, Imperial College London 180 Queen's Gate, London SW7 2BZ, UK;Cancer Research UK, Advanced Computation Laboratory, London Research Institute, Lincoln's Inn Fields Laboratories 44 Lincoln's Inn Fields, London WC2A 3PX, UK;Biochemistry Building, Division of Molecular Biosciences, Imperial College London SW7 2AZ, UK

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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Abstract

Motivation: The output of a bioinformatic tool such as BLAST must usually be interpreted by an expert before reliable conclusions can be drawn. This may be based upon the expert's experience, additional data and statistical analysis. Often the process is laborious, goes unrecorded and may be biased. Argumentation is an established technique for reasoning about situations where absolute truth or precise probability is impossible to determine. Results: We demonstrate the application of argumentation to 3D-PSSM, a protein structure prediction tool. The expert's interpretation of results is represented as an argumentation framework. Given a 3D-PSSM result, an automated procedure constructs arguments for and against the conclusion that the result is a good predictor of protein structure. In addition to capturing the unique expertise of the author of 3D-PSSM for distribution to users, an improvement in recall of 5--10 percentage points is achieved. This technique can be applied to a wide range of bioinformatic tools. Availability: Example public server and benchmarking data are available at http://www.sbg.bio.ic.ac.uk/~brj03/argumentation/paper/. Source code available on request. Contact: m.sternberg@imperial.ac.uk