Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations
作者机构：School of Computer ScienceQufu Normal UniversityRizhao 276826China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2021年第36卷第2期
页 面：276-287页
核心收录：
学科分类：1001[医学-基础医学(可授医学、理学学位)] 100102[医学-免疫学] 10[医学]
基 金：This work was supported by the National Natural Science Foundation of China under Grant Nos.61902215 61872220 and 61701279
主 题：miRNA-disease association logistic function Gaussian interaction profile weighted K-nearest known neighbour bi-random walk
摘 要：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.
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