Concentration optimization of combinatorial drugs using Markov chain-based models

BMC Bioinformatics

Table 1 Performance comparison of six algorithms in simulations

	GG		MGG		DE		CAPR		s-FSC		Markov chain
	Success rate	# of iters	Success rate	# of iters	Success rate	# of iters	Success rate	# of ites	Success rate	# of ites	Success rate	# of iters
f₁
λ = 0.95	0.67	30.6	0.76	25.2	0.72	34.4	0.89	21.2	0.92	27.5	1.00	8.6
P = 5%	0.88	18.2	0.89	20.3	0.69	30.3	0.87	17.5	0.91	22.5	1.00	5.3
f₂
λ = 0.95	0.68	32.6	0.70	25.2	0.65	28.7	0.91	22.2	0.96	12.6	1.00	9.2
P = 5%	0.89	17.2	0.80	20.3	0.72	23.3	0.92	15.6	0.95	10.6	1.00	7.3
f₃
λ = 0.95	0.60	27.6	0.78	29.2	0.65	27.7	0.91	28.2	0.97	29.2	1.00	17.2
P = 5%	0.84	18.2	0.88	21.3	0.75	30.3	0.92	19.6	0.96	22.5	1.00	12.3
f₄
λ = 0.95	0.82	85.6	0.92	60.2	0.78	88.4	0.85	78.2	0.88	71.2	1.00	43.6
P = 5%	0.95	35.2	0.99	25.3	0.62	43.3	0.72	44.5	0.78	40.5	1.00	22.3
f₅
λ = 0.95	0.13	45.1	1.00	53.4	0.26	43.4	0.33	38.4	0.40	39.4	1.00	15.2
P = 5%	0.15	32.3	1.00	45.7	0.09	47.7	0.21	45.2	0.32	48.3	1.00	19.9
f₆
λ = 0.95	0.72	95.1	0.87	54.2	0.72	82.4	0.88	71.2	0.78	72.3	1.00	33.6
P = 5%	0.81	30.2	0.91	24.3	0.56	41.9	0.67	41.8	0.62	47.7	1.00	18.3

ISSN: 1471-2105