miRNA-Disease Association Prediction with Collaborative Matrix Factorization
As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to id...
Saved in:
Main Authors: | Zhen Shen, You-Hua Zhang, Kyungsook Han, Asoke K. Nandi, Barry Honig, De-Shuang Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2017/2498957 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
by: Jihwan Ha
Published: (2025-01-01) -
Prediction of circRNA-miRNA Associations Based on Network Embedding
by: Wei Lan, et al.
Published: (2021-01-01) -
miRNA-451a and miRNA-125a Expression Levels in Ankylosing Spondylitis: Impact on Disease Diagnosis, Prognosis, and Outcomes
by: Dina Salem Fotoh, et al.
Published: (2020-01-01) -
A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis
by: Jiwei Xue, et al.
Published: (2017-01-01) -
Decoding oral cancer: insights from miRNA expression profiles and their regulatory targets
by: Xin Wang, et al.
Published: (2025-01-01)