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

BMC Bioinformatics

Table 1 Accuracy of three different machine learning prediction algorithms – J48 Decision Tree, Naïve Bayes and SVM with SMO training – using binary H/P sequences.^a

	J48	Naïve Bayes	SMO
a) Sequences folding to the top 10% and the bottom 10% of designable conformations for the hexagon	96.8% correct	95.8% correct	98.3% correct
	AUC .97	AUC 0.99	AUC 0.98
	Sens: 1.0	Sens: 1.0	Sens: 0.997
	Spec: 0.94	Spec: 0.92	Spec: 0.97
b) Sequences folding to the top 10% and the bottom 10% of designable conformations for the triangle	92.7% correct	82.4% correct	95.0% correct
	AUC 0.93	AUC 0.92	AUC 0.95
	Sens: 0.93	Sens: 0.76	Sens: 0.92
	Spec: 0.92	Spec: 0.86	Spec: 0.97

^a We compare random subsets of sequences corresponding to the top 10% and the bottom 10% of designable structures for the a) hexagon, and b) triangle. Prediction accuracy and area under the curve (AUC), sensitivity (Sens) and specificity (Spec) for each method are given.