Table 4. Performances of the SVM models during 10-fold cross-validation using LOTOCV method.

		No. of ASP-siRNAs		Pearson Correlation Coefficient (PCC) During 10nCV and IV
S. No.	Gene Name	Training Dataset	Validation Dataset	10nCV	IV
1	APP	907	15	0.71	0.88
2	AR	912	10	0.71	0.19
3	COL1A1	912	10	0.71	0.49
4	COL3A1	903	19	0.71	0.34
5	COL6A3	911	11	0.70	0.24
6	COL7A1	903	19	0.71	0.55
7	HTT	883	39	0.56	0.28
8	KRAS	844	78	0.68	0.31
9	KRT12	884	38	0.71	0.48
10	KRT5	884	38	0.71	0.24
11	KRT6a	903	19	0.70	0.31
12	KRT9	830	92	0.63	0.26
13	LRRK2	901	21	0.71	0.26
14	Others	844	78	0.74	0.20
15	P. Luciferase	865	57	0.71	0.23
16	PPIB	695	227	0.53	0.61
17	PRNP	904	18	0.71	0.79
18	PSEN1	903	19	0.43	0.30
19	SNCA	906	16	0.71	0.50
20	SOD1	881	41	0.53	0.34
21	TGFBI	903	19	0.55	0.64
22	TP63	884	38	0.58	0.33

ASP-siRNAs targeting a particular gene are assigned to the validation dataset, while sequences from other genes were assigned to the training set. Validation of the models was done using respective gene in the independent validation set. Standard HGNC gene symbols have been used. PCC is between the actual and observed Eff^mut. The training dataset is used to train different predictive models, while independent validation datasets were not used in any training algorithms. S.No., Serial number; 10nCV, ten-fold cross-validation; IV, independent validation.