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



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, and 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.



 In this paper, we propose a novel method, named as a constrained strategy for
predicting microRNA-disease associations called CPMDA, which can predict some potential
essential microRNAs only with few known associations.



We firstly construct a 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 shows that CPMDA
has a strong effect on microRNAs with few known associations. In case studies, CPMDA also
demonstrated the effectiveness to infer unknown microRNA-disease associations for those novel
diseases and microRNAs.
