IPF-LASSO: Integrative -Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Table 1

Combinations of , , , , , and used for the main design. All other parameters are fixed (, , ). For each setting, datasets are successively generated.

Setting A	1000	1000	10	10	0.5	0.5
Setting B	100	1000	3	30	0.5	0.5
Setting C	100	1000	10	10	0.5	0.5
Setting D	100	1000	20	0	0.3
Setting E	20	1000	3	10	1	0.3
Setting F	20	1000	15	3	0.5	0.5