Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

BMC Bioinformatics

Table 4 Accuracy of machine learning predictions classifying sequences folding to the least designable conformations among random binary sequences for a) hexagonal and b) triangular shapes.^a

	J48	Naïve Bayes	SMO
a) Sequences folding to the bottom 10% of designable structures vs. random binary sequences of length 19 for the hexagon	57.5% correct	55.6% correct	57.9% correct
	AUC 0.58	AUC 0.59	AUC 0.58
	Sens: 0.62	Sens: 0.55	Sens: 0.61
	Spec: 0.56	Spec: 0.55	Spec: 0.57
b) Sequences folding to the bottom 10% of designable structures vs. random binary sequences of length 21 for the triangle	50.1% correct	52.3% correct	56.0% correct
	AUC 0.50	AUC 0.53	AUC 0.56
	Sens: 0.54	Sens: 0.67	Sens: 0.59
	Spec: 0.53	Spec: 0.54	Spec: 0.58

^a Values of prediction accuracy and area under the curve (AUC), sensitivity (Sens) and specificity (Spec) for each method are given.

ISSN: 1471-2105