MDA-TOEPGA:A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.

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.

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

基于自编码器的疾病相关miRNAs的预测方法

MicroRNAs(miRNAs)是一类由内源基因编码的长度约为22个核苷酸的非编码单链RNA分子,它们在动植物中参与转录后基因表达调控。大量研究表明,miRNAs在包括肿瘤在内的多种复杂疾病发生、发展过程中扮演着重要的角色。因此,识别疾病相关的miRNAs对研究疾病的机理及治疗具有重要意义。鉴于湿实验验证方法存在耗时长、成本高的缺点,当前许多研究工作聚焦于开发高效计算模型,识别新的miRNA-disease关联关系。该研究提出一种基于自编码器数据驱动的模型,预测miRNA-disease关联关系。结果表明,作者预测的疾病相关miRNAs在HMDD数据库中对应的疾病相关miRNAs列表上显著富集。此外,通过对排名靠前的miRNAs分析,发现这些miRNAs具有重要的生物学功能,同时对于疾病的分类表现出较高的精度。总之,文章提出的模型,对于疾病相关miRNAs的发现具有重要的辅助作用。

Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.