Algorithm 1: LFEMDA, predicting miRNA-disease association by latent feature extraction with positive samples

Input:MS: m*m miRNAs functional similarity matrix   DS: d*d disease semantic similarity matrix   R: the experimentally confirmed miRNAs-disease association matrix Paramter: k: hidden space dimension   ${\lambda }_0$: second normal form regularization coefficient   ${\lambda }_1$: the distance coefficient between expression matrix inner product of miRNAs on the hidden space and MS   ${\lambda }_2$: the distance coefficient between expression matrix inner product of diseases on the hidden space and DS   ${\mu }_1$: the distance coefficient between expression matrix of miRNAs on the hidden space and auxiliary matrix X   ${\mu }_2$: the distance coefficient between expression matrix of diseases on the hidden space and auxiliary matrix Y

Output:${\mathrm{R}}^{\prime }$: the predicted miRNAs-disease association matrix Initialize the vector matrices M, D, and the auxiliary vectors X, Y of miRNAs and diseases $\Delta \leftarrow \infty$, $loss\leftarrow \infty$ while Δ>ε:   update M, given current D, X and Y, using Formula (11)   update D, given current M, X and Y, using Formula (12)   calculate current X based on the new M   calculate current Y based on the new D   calculate loss_new using Formula (9)   $\Delta \leftarrow loss\_new-loss$   $loss\leftarrow loss\_new$ End while $R^{\prime }=M^{T}D$