title: Similarity computation strategies in the microRNA-disease network: a survey.
abstract: Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
authors.forename: Jinjin
authors.initials: J
authors.lastname: Li
authors.forename: Li
authors.initials: L
authors.lastname: Song
authors.forename: Xiangxiang
authors.initials: X
authors.lastname: Zeng
authors.forename: Guohua
authors.initials: G
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000235:genetics*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D035683:MicroRNAs / Q000235:genetics*; D010641:Phenotype; D011795:Surveys and Questionnaires; D049490:Systems Biology
chemical_list: D035683:MicroRNAs
keywords: machine learning; microRNA; microRNA–disease relationship; network