Enhanced protein fold recognition through a novel data integration approach

BMC Bioinformatics

Table 1 Performance with individual and all data sources

Data sources	MKLdiv-dc	MKLdiv-conv	SimpleMKL	VBKC	MKL-RKDA
Amino acid composition (C)	51.69	51.69	51.83	51.2 ± 0.5	45.43
Predicted secondary structure (S)	40.99	40.99	40.73	38.1 ± 0.3	38.64
Hypdrophobicity (H)	36.55	36.55	36.55	32.5 ± 0.4	34.20
Polarity (P)	35.50	35.50	35.50	32.2 ± 0.3	30.54
van der Walls volume (V)	37.07	37.07	37.85	32.8 ± 0.3	30.54
Polarizability (Z)	37.33	37.33	36.81	33.2 ± 0.4	30.28
PseAA λ = 1 (L1)	44.64	44.64	45.16	41.5 ± 0.5	36.55
PseAA λ = 4 (L4)	44.90	44.90	44.90	41.5 ± 0.4	38.12
PseAA λ = 14 (L14)	43.34	43.34	43.34	38 ± 0.2	40.99
PseAA λ = 30 (L30)	31.59	31.59	31.59	32 ± 0.2	36.03
SW with BLOSUM62 (SW1)	62.92	62.92	62.40	59.8 ± 1.9	61.87
SW with PAM50 (SW2)	63.96	63.96	63.44	49 ± 0.7	64.49
All data sources	73.36	71.01	66.57	68.1 ± 1.2	68.40
Uniform weighted	68.40	68.40	68.14	-	66.06

The results of VBKC are cited from [21]. The results not employed there are denoted by '-'. The best result for each kernel learning method is marked in bold.

ISSN: 1471-2105