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stitution groups that produces more general reactive
motifs. Used as input features of SVM, generated
FCL-based reactive motifs provide highest coverage,
F-measure, precision and recall values without affect-
ing the FP and FN values in the prediction model.
Rule-based learning methods can be investigated
to provide meaningful and understandable informa-
tion to biologists. Among rule-based methods, asso-
ciative classification technique (Liu et al., 1998; Li
et al., 2001; Belohlvek et al., 2007) is recognized to
be more accurate over traditional classification tech-
niques i.e. C4.5 for very large number of classes to
predicted. In the future work, we will increase both
accuracy and explanatory ability of the protein func-
tion classification model using reactive motifs as in-
put features to an associative classification.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the Thailand Re-
search Fund (TRF) and Kasetsart University for finan-
cial support through the Royal Golden Jubilee Ph.D.
scholarship program (1.0.KU/49/A.1).
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