SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction
Joint Authors
Li, Xiaoying
Lin, Yaping
Gu, Changlong
Li, Zejun
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-21
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases.
Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases.
However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive.
Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs.
This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP).
The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations.
SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.
American Psychological Association (APA)
Li, Xiaoying& Lin, Yaping& Gu, Changlong& Li, Zejun. 2018. SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction. BioMed Research International،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1127488
Modern Language Association (MLA)
Li, Xiaoying…[et al.]. SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction. BioMed Research International No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1127488
American Medical Association (AMA)
Li, Xiaoying& Lin, Yaping& Gu, Changlong& Li, Zejun. SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1127488
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1127488