HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneou...

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Veröffentlicht in:Oncotarget Jg. 7; H. 40; S. 65257
Hauptverfasser: Chen, Xing, Yan, Chenggang Clarence, Zhang, Xu, You, Zhu-Hong, Huang, Yu-An, Yan, Gui-Ying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 04.10.2016
Schlagworte:
Computational Biology - methods
Computer Simulation
Humans
MicroRNAs
Models, Theoretical
Neoplasms - genetics
microRNA
microRNA-disease association
disease
heterogeneous network
similarity
ISSN:1949-2553, 1949-2553
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Zusammenfassung:Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.
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ISSN:1949-2553
1949-2553
DOI:10.18632/oncotarget.11251