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

BMC Bioinformatics

Table 5 Accuracy of machine learning predictions.^a

	J48	Naïve Bayes	SMO
a) Sequences folding to the top 10% of designable structures vs. sequences folding to the bottom 10% of designable structures for both shapes	69.5% correct	65.0% correct	65.6% correct
	AUC 0.73	AUC 0.69	AUC 0.67
	Sens: 0.67	Sens: 0.66	Sens: 0.71
	Spec: 0.71	Spec: 0.65	Spec: 0.64
b) Sequences folding to the top 10% of designable structures of hexagonal shape vs. sequences folding to the bottom 10% of designable structures in the triangular shape	98.1% correct	84.9% correct	87.0% correct
	AUC 0.99	AUC 0.92	AUC 0.87
	Sens: 0.98	Sens: 0.82	Sens: 0.84
	Spec: 0.98	Spec: 0.90	Spec: 0.92
c) Sequences folding to the top 10% of designable structures of triangular shape vs. sequences folding to the bottom 10% of designable structures in the hexagonal shape	98.0% correct	65.8% correct	64.3% correct
	AUC 0.99	AUC 0.70	AUC 0.63
	Sens: 0.98	Sens: 0.64	Sens: 0.75
	Spec: 0.98	Spec: 0.72	Spec: 0.66

^a For classifying a)sequences folding to highly-designable conformations for the hexagonal and triangular shapes against sequences folding to the least designable conformations for these two shapes; b)sequences folding to the most designable conformations of the hexagonal shape against sequences folding to the least designable conformations of the triangular shape and c)sequences folding to the most designable conformations of the triangular shape against sequences folding to the least designable conformations of the hexagonal shape. Prediction accuracy and area under the curve (AUC), sensitivity (Sens) and specificity (Spec) for each method are given.

ISSN: 1471-2105