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

BMC Bioinformatics

Table 2 Accuracy of three different machine learning prediction algorithms (J48 Decision Tree, Naïve Bayes and SVM with SMO training) using the frequencies of all possible short tripeptide binary segments.^a

	J48	Naïve Bayes	SMO
a) Sequences folding to the top 10% and the bottom 10% of designable conformations for the hexagon	89.7% correct	78.8% correct	91.0% correct
	AUC 0.95	AUC 0.92	AUC 0.91
	Sens: 0.91	Sens: 0.85	Sens: 0.84
	Spec: 0.90	Spec: 0.77	Spec: 0.91
b) Sequences folding to the top 10% and the bottom 10% of designable conformations for the triangle	67.8% correct	56.7% correct	57.8% correct
	AUC 0.69	AUC 0.61	AUC 0.58
	Sens: 0.68	Sens: 0.58	Sens: 0.64
	Spec: 0.68	Spec: 0.57	Spec: 0.57

^a We compare random subsets of sequences corresponding to the top 10% and the bottom 10% of designabile 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.

ISSN: 1471-2105