Capturing expert knowledge with argumentation: a case study in bioinformatics

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....

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Bibliographic Details
Published in:Bioinformatics 2006-04, Vol.22 (8), p.924-933
Main Authors: Jefferys, Benjamin R., Kelley, Lawrence A., Sergot, Marek J., Fox, John, Sternberg, Michael J. E.
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Bioinformatics
Biological and medical sciences
Computational Biology - methods
Computer Simulation
Expert Systems
Fundamental and applied biological sciences. Psychology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Chemical
Molecular Sequence Data
Pattern Recognition, Automated - methods
Protein Conformation
Proteins - analysis
Proteins - chemistry
Sequence Analysis, Protein - methods
Statistical analysis
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Summary: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 . Source code available on request. Contact:m.sternberg@imperial.ac.uk
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btl018