Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background:MicroRNAs(miRNAs)are a significant type of non-coding RNAs,which usually were encoded by endogenous genes with about?22 nt nucleotides.Accumulating biological experiments have shown that miRNAs have close associations with various human diseases.Although traditional experimental methods achieve great successes in miRNA-disease interaction identification,these methods also have some limitations.Therefore,it is necessary to develop computational method to predict miRNA-disease interactions.Methods:Here,we propose a computational framework(MDVSI)to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity.Firstly,the CosRA index is utilized to measure miRNA similarity based on network topological feature.Then,in order to enhance the reliability of miRNA similarity,the functional similarity and CosRA similarity are integrated based on linear weight method.Further,the potential miRNA-disease associations are predicted by using recommendation method.In addition,in order to overcome limitation of recommendation method,for new disease,a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.Results:To evaluate the performance of different methods,we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods.The experimental result shows that MDVSI achieves an AUC of 0.91,which is at least 0.012 higher than other compared methods.Conclusions:In summary,we propose a computational framework(MDSVI)for miRNA-disease interaction prediction.The experiment results demonstrate that it outperforms other the-state-of^the-art methods.Case study shows that it can effectively identify potential miRNA-disease interactions.

Privacy-preserving decision tree for epistasis detection

The interaction between gene loci,namely epistasis,is a widespread biological genetic phenomenon.In genome-wide association studies(GWAS),epistasis detection of complex diseases is a major challenge.Although many approaches using statistics,machine learning,and information entropy were proposed for epistasis detection,the privacy preserving for single nucleotide polymorphism(SNP)data has been largely ignored.Thus,this paper proposes a novel two-stage approach.A fusion strategy assists in combining and sorting the SNPs importance scores obtained by the relief and mutual information,thereby obtaining a candidate set of SNPs.This avoids missing some SNPs with strong interaction.Furthermore,differentially private decision tree is applied to search for SNPs.This achieves the efficient epistasis detection of complex diseases on the basis of privacy preserving compared with heuristic methods.The recognition rate on simulation data set is more than 90%.Also,several susceptible loci including rs380390 and rs1329428 are found in the real data set for Age-related Macular Degeneration(AMD).This demonstrates that our method is promising in epistasis detection.

Erratum to:Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

The original version of this article unfortunately contained a mistake in the name of the last author.The corrected name is Yi-Ping Phoebe Chen.

Privacy-preserving decision tree for epistasis detection

The interaction between gene loci,namely epistasis,is a widespread biological genetic phenomenon.In genome-wide association studies(GWAS),epistasis detection of complex diseases is a major challenge.Although many approaches using statistics,machine learning,and information entropy were proposed for epistasis detection,the privacy preserving for single nucleotide polymorphism(SNP)data has been largely ignored.Thus,this paper proposes a novel two-stage approach.A fusion strategy assists in combining and sorting the SNPs importance scores obtained by the relief and mutual information,thereby obtaining a candidate set of SNPs.This avoids missing some SNPs with strong interaction.Furthermore,differentially private decision tree is applied to search for SNPs.This achieves the efficient epistasis detection of complex diseases on the basis of privacy preserving compared with heuristic methods.The recognition rate on simulation data set is more than 90%.Also,several susceptible loci including rs380390 and rs1329428 are found in the real data set for Age-related Macular Degeneration(AMD).This demonstrates that our method is promising in epistasis detection.