Open AccessGIMDA: Graphlet interaction‐based MiRNA‐disease association prediction
Author(s) -
Chen Xing,
Guan NaNa,
Li JianQiang,
Yan GuiYing
Publication year - 2018
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.13429
Subject(s) - semantic similarity , microrna , disease , computational biology , cross validation , similarity (geometry) , bioinformatics , medicine , biology , artificial intelligence , computer science , genetics , pathology , gene , image (mathematics)
MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA‐Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA‐disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave‐one‐out cross‐validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five‐fold cross‐validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA‐disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.