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Enhanced Encoding with Improved Fuzzy Decision Tree Testing Using CASP Templates

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3 Author(s)
Anjum Chida ; Department of Computer Science, Georgia State University, Atlanta, Georgia 30302-3994 USA ; Yan-Qing Zhang ; Robert Harrison

A novel protein model assessment technique using an improved fuzzy decision tree was tested using CASP8 and CASP9 templates. The testing was conducted in three phases. In the first two phases, templates were classified without explicit scoring. In phase three, a new scoring method was created for performance evaluation. The new protein model assessment technique was compared with several common prominent model assessment techniques for CASP competitions. The performance was analyzed based on correlation between our scores and GDT_TS scores of the templates. Finally, it is concluded that although the novel protein model assessment technique resulted in reduced correlation when compared to best competitors in CASP, its uniqueness in using the improved fuzzy decision tree and a single model stands as a new paradigm in the protein model assessment field.

Published in:

IEEE Computational Intelligence Magazine  (Volume:7 ,  Issue: 4 )