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

BMC Bioinformatics

Table 3 Accuracy of machine learning predictions classifying sequences folding to the most designable conformations among random binary sequences for a) hexagonal and b) triangular shapes.^a

	J48	Naïve Bayes	SMO
a) Sequences folding to the top 10% of designable structures vs. random binary sequences of length 19 for the hexagon	97.2% correct	94.2% correct	97.3% correct
	AUC 0.97	AUC 0.98	AUC 0.98
	Sens: 1.0	Sens: 1.0	Sens: 0.997
	Spec: 0.94	Spec: 0.89	Spec: 0.95
b) Sequences folding to the top 10% of designable structures vs. random binary sequences of length 21 for the triangle	90.3% correct	84.4% correct	95.2% correct
	AUC 0.91	AUC 0.92	AUC 0.95
	Sens: 0.93	Sens: 0.92	Sens: 0.97
	Spec: 0.90	Spec: 0.82	Spec: 0.94

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

ISSN: 1471-2105