Submit Manuscript  

Article Details


A Constrained Probabilistic Matrix Decomposition Method for Predicting miRNA-disease Associations

Author(s):

Xinguo Lu, Yan Gao, Zhenghao Zhu*, Li Ding, Xinyu Wang, Fang Liu and Jinxin Li   Pages 1 - 10 ( 10 )

Abstract:


MicroRNA is a type of non-coding RNA molecule whose length is about 22 nucleotides. The growing evidence shows that microRNA makes critical regulations in the development of complex diseases, such as cancers, cardiovascular diseases. Predicting potential microRNA-disease associations can provide a new perspective to achieve a better scheme of disease diagnosis and prognosis. However, there is a challenge to predict some potential essential microRNAs only with few known associations. To tackle this, we propose a novel method, named as constrained strategy for predicting microRNA-disease associations called CPMDA, in heterogeneous omics data. Here, we firstly construct disease similarity network and microRNA similarity network to preprocess the microRNAs with none available associations. Then, we apply probabilistic factorization to obtain two feature matrices of microRNA and disease. Meanwhile, we formulate a similarity feature matrix as constraints in the factorization process. Finally, we utilize obtained feature matrixes to identify potential associations for all diseases. The results indicate that CPMDA is superior over other methods in predicting potential microRNA-disease associations. Moreover, the evaluation show that CPMDA has a strong effect on microRNAs with few known associations. In case studies, CPMDA also demonstrated the effectiveness to infer unknown microRNAdisease associations for those novel diseases and microRNAs.

Keywords:

Matrix decomposition, miRNA-disease association, disease similarity, miRNA similarity

Affiliation:

College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha, College of Computer Science and Electronic Engineering, Hunan University, Changsha



Read Full-Text article