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Code and Datasets of Paper Fusing Multiple Similarity Kernels to Accurately Discover MiRNA-disease Association

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SKFMDA

Code and Datasets of Paper--SKFMDA: Similarity Kernel Fusion for Accurately Discovering MiRNA-Disease Association

No1. For fold_5.m, local_LOOCV.m and global_LOOCV.m

Input: MiRNA_functional_similarity_Metrix.mat MiRNA_sequence_similarity_Metrix.mat Disease_semantic similarity Metrix.mat Disease_functional_similarity_Metrix.mat Adjacency_Metrix.mat

Output: A novel Adjacency_Metrix

No2. For global_validation_demo.mat and local_validation_demo.mat

Mind: all associations that include in the HMDD, dbDEMC and miR2Disease are necessary.

supporting information file include all the predicted associations by using local validation and global validation.

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Code and Datasets of Paper Fusing Multiple Similarity Kernels to Accurately Discover MiRNA-disease Association

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