ES-MDA: Enhanced Similarity-based MiRNA-Disease Association

被引:2
作者
Xu, Li [1 ]
Jiang, Ge-Ning [1 ]
机构
[1] Tongji Univ, Shanghai Pulm Hosp, Dept Thorac Surg, Shanghai 201804, Peoples R China
关键词
microRNA; disease; lung cancer; network consistency projection; biological resources; pathology; MICRORNA EXPRESSION; ESOPHAGEAL CANCER; CELL-GROWTH; PREDICTION; METHODOLOGY; DIAGNOSIS; PROFILES; FEATURES; MIR-21;
D O I
10.2174/1389203721666200911151723
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Accumulating evidence demonstrate that miRNAs can he treated as critical biomarkers in various complex human diseases. Thus, the identifications on potential miRNA-disease associations have become a hotpot for providing better understanding of disease pathology in this field. Recently, with various biological datasets, increasingly computational prediction approaches have been designed to uncover disease-related miRNAs for further experimental validation. To improve the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease associations to enhance the miRNA functional similarity network and disease similarities of known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated that machine learning methods would become an effective biological resource for clinical experimental guidance.
引用
收藏
页码:1060 / 1067
页数:8
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