Meta-path Based MiRNA-Disease Association Prediction

• Published in: Database Systems for Advanced Applications Vol. 11448; pp. 34 - 48
• Main Authors: Lv, Hao, Li, Jin, Zhang, Sai, Yue, Kun, Wei, Shaoyu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Association prediction; Disease; Heterogeneous Information Network; Link prediction; Meta-paths; MiRNA
• ISBN: 3030185893; 9783030185893
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-18590-9_3

Predicting the association of miRNA with disease is an important research topic of bioinformatics. In this paper, a novel meta-path based approach MPSMDA is proposed to predict the association of miRNA-disease. MPSMDA uses experimentally validated data to build a miRNA-disease heterogeneous information network (MDHIN). Thus, miRNA-disease association prediction is transformed into a link prediction problem on a MDHIN. Meta-path based similarity is used to measure the miRNA-disease associations. Since different meta-paths between a miRNA and a disease express different latent semantic association, MPSMDA make full use of all possible meta-paths to predict the associations of miRNAs with diseases. Extensive experiments are conducted on real datasets for performance comparison with existing approaches. Two case studies on lung neoplasms and breast neoplasms are also provided to demonstrate the effectiveness of MPSMDA.