Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

BMC Bioinformatics

Table 4 Summary of leaves providing maximum separation between amyloidogenic and non-amyloidogenic derivatives of different germline sets*

Leaf	J00248	M30446	X72813	X93620	X93627	X93632	X93640	Z22188	Z22191	Z22197	Z22208	Z73673
1	0.091	0.009	0.024	-0.016	0.042	0.046	-0.001	0.044	0.028	0.036	-0.032	-0.036
2	-0.030	0.008	0.009	-0.013	-0.135	-0.093	0.022	-0.075	0.073	0.089	0.052	0.062
3	-0.038	-0.001	0.071	-0.035	-0.038	-0.209	0.100	-0.085	-0.035	-0.017	0.068	0.003
4	-0.058	0.030	-0.145	-0.017	0.053	0.116	-0.008	-0.123	0.058	-0.198	0.039	0.014
5	-0.044	0.007	0.056	0.065	0.018	0.070	-0.009	-0.081	-0.092	-0.057	-0.025	0.008
6	0.132	-0.028	0.043	0.004	0.026	0.070	0.012	0.079	0.058	0.026	-0.006	-0.029
7	-0.058	-0.031	-0.054	-0.052	-0.048	0.000	-0.018	0.102	-0.082	0.158	-0.105	-0.016
8	0.007	0.006	0.066	0.063	0.083	0.00	-0.099	0.139	-0.011	-0.037	0.009	0.040

* Values were obtained by substracting the percentage of mutations in non-amyloidogenic derivatives from the percentage of mutations in amyloidogenic derivatives terminating in a given leaf. Minimum and maximum values per germline set, which were used to identify the paths where scores were decreased and boosted, respectively, are shown in italics and boldface, respectively.

ISSN: 1471-2105