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

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BIOCELL 2022年第8期46卷 1925-1933页

作者： BUWEN CAO JIAWEI LUO SAINAN XIAO XIANGJUN ZHOU College of Information and Electronic Engineering Hunan City UniversityYiyang413000China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China

The association between mirna and disease has attracted more and more *** 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 *** there is strong need of new computational method to identify the associations from mirna group *** 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 *** understand the mirna functional module in diseases,the case study is *** have been compared MDA-TOEPGA with several state-of-the-art functional module *** 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 ***,our study showed that MDA-TOEPGA is a promising method to investigate mirna-disease association from the landscapes of functional module.

关键词： mirna functional module mirna-disease association Two-objective Evolutionary programming genetic algorithm

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DMFVAE: mirna-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 186912-186912页

作者： WEI Pijing WANG Qianqian GAO Zhen CAO Ruifen ZHENG Chunhou Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University

MicroRNAs (mirnas) are closely related to numerous complex human diseases, therefore, exploring mirna-disease associations (MDAs) can help people gain a better understanding of complex disease mechanism. An increasing number of computational methods have been developed to predict MDAs. However, the sparsity of the MDAs may hinder the performance of many methods. In addition, many methods fail to capture the nonlinear relationships of mirna-disease network and inadequately leverage the features of network and neighbor nodes. In this study, we propose a deep matrix factorization model with variational autoencoder (DMFVAE) to predict potential MDAs. DMFVAE first decomposes the original association matrix and the enhanced association matrix, in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method, to obtain sparse vectors and dense vectors, respectively. Then, the variational encoder is employed to obtain the nonlinear latent vectors of mirna and disease for the sparse vectors, and meanwhile, node2vec is used to obtain the network structure embedding vectors of mirna and disease for the dense vectors. Finally, sample features are acquired by combining the latent vectors and network structure embedding vectors, and the final prediction is implemented by convolutional neural network with channel attention. To evaluate the performance of DMFVAE, we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs well. Furthermore, case studies on lung neoplasms, colon neoplasms, and esophageal neoplasms confirm the ability of DMFVAE in identifying potential mirnas for human diseases.

关键词： mirna-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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Logistic Weighted Profile-Based Bi-Random Walk for Exploring mirna-disease associations

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Journal of Computer Science & Technology 2021年第2期36卷 276-287页

作者： Ling-Yun Dai Jin-Xing Liu Rong Zhu Juan Wang Sha-Sha Yuan School of Computer Science Qufu Normal UniversityRizhao 276826China

MicroRNAs(mirnas)exert an enormous influence on cell differentiation,biological development and the onset of *** 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 *** accuracy is critical for *** date,many algorithms have been proposed to infer novel ***,they may still have some *** this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known *** this method,three networks(i.e.,a mirna functional similarity network,a disease semantic similarity network and a known MDA network)are constructed *** 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 ***,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false ***,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the mirna network and the disease ***,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.

关键词： mirna-disease association logistic function Gaussian interaction profile weighted K-nearest known neighbour bi-random walk

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

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BIOCELL 2022年第3期46卷 837-845页

作者： BUWEN CAO JIAWEI LUO SAINAN XIAO KAI ZHAO SHULING YANG College of Information and Electronic Engineering Hunan City UniversityYiyang413000China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China

Identifying associations between microRNAs(mirnas)and diseases is very important to understand the occurrence and development of human ***,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 ***,little approaches infer disease-related mirnas depending on the network topological features without the functional similarity of *** 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 ***-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case *** 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 ***,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been *** provides biologists new clues for diagnosing breast and lung cancer.

关键词： disease-related mirna mirna-disease association Functional similarity Network topological similarity

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