Table 2.

Parameter settings

Method	Parameter setting
LPI-NRLMF	Cfix = 5, num_factors = 10, K1 = 5, max_iter = 100
	Lambda_t = 0.625, alpha = 0.1, beta = 0.1
	K2=5, theta = 1.0, lambda_d = 0.625
Capsule-LPI	EPOCH = 30, lr = 0.001, BATCH_SIZE = 100
LPI-CNNCP	Filters1 = 24, kernel_size1 = (49, 10)
	Kernel_size2 = (64, 10), strides2 = (1, 3)
	Strides1 = (1, 1), filters2 = 24
LPI-HyADBS	DNN: Adam(model.parameters(), lr = 0.0001),
	Loss_fn=BCELoss(), batch = 128, epochs = 100
	XGBoost: learning_rate = 0.1, n_estimators = 100
	Objective =“binary:logistic”, max_depth = 6
	C-SVM: kernel=“rbf”, gamma = “auto”,
	Probability = True, colsample_btree = 0.8
	$\alpha =0.4$, $\beta =0.3$, $\theta =0.3$
LPLNP	Neighbor_num = [6, 23, 100, num of lncRNA-100, 100],
	Regulation = ’regulation2’, alpha = [0.5, 0.3, 0.7, 0.1, 0.9]