Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Table 1

One-shot learning.

Dataset	Algorithm	Avg. Acc.	St. Dev.	Sensitivity	Specificity
HIV-1	AdaBoost	68.84	4.77	0.87	0.63
	LogitBoost	75.93	3.90	0.72	0.86
	SVM	68.65	3.50	0.66	0.75
	RF	79.28	1.96	0.88	0.76
	DT	77.57	1.21	0.73	0.81
	T1 NN	73.13	2.57	0.69	0.76
	T1 RF	74.64	3.24	0.65	0.74
T4	AdaBoost	85.10	0.14	0.98	0.11
	LogitBoost	85.65	0.31	0.97	0.20
	SVM	86.88	0.24	0.99	0.17
	RF	87.12	0.44	0.97	0.30
	DT	85.33	0.56	0.93	0.34
	T1 NN	75.46	6.99	0.80	0.46
	T1 RF	85.02	7.44	0.94	0.35
LAC	AdaBoost	60.53	0.31	0.99	0.12
	LogitBoost	71.88	0.58	0.91	0.48
	SVM	72.15	0.16	0.88	0.52
	RF	80.80	0.37	0.86	0.75
	DT	78.71	0.34	0.83	0.74
	T1 NN	65.23	3.58	0.76	0.39
	T1 RF	77.73	3.64	0.78	0.77

Results of the AdaBoost, LogitBoost, SVM, random forest, decision tree, and transduction T1 algorithms. Using one-shot learning, no selectivity, and 4-fold cross-validation for the HIV-1 dataset and 10-fold cross-validation for T4 and LAC datasets.