Abstract
BackgroundBiological evidence has shown that microRNAs(miRNAs) are greatly implicated in various biological progresses involved in human diseases. The identification of miRNA-disease associations(MDAs) is beneficial to disease diagnosis as well as treatment. Due to the high costs of biological experiments, it attracts more and more attention to predict MDAs by computational approaches.ResultsIn this work, we propose a novel model MTFMDA for miRNA-disease association prediction by matrix tri-factorization, based on the known miRNA-disease associations, two types of miRNA similarities, and two types of disease similarities. The main idea of MTFMDA is to factorize the miRNA-disease association matrix to three matrices, a feature matrix for miRNAs, a feature matrix for diseases, and a low-rank relationship matrix. Our model incorporates the Laplacian regularizers which force the feature matrices to preserve the similarities of miRNAs or diseases. A novel algorithm is proposed to solve the optimization problem.ConclusionsWe evaluate our model by 5-fold cross validation by using known MDAs from HMDD V2.0 and show that our model could obtain the significantly highest AUCs among all the state-of-art methods. We further validate our method by applying it on colon and breast neoplasms in two different types of experiment settings. The new identified associated miRNAs for the two diseases could be verified by two other databases including dbDEMC and HMDD V3.0, which further shows the power of our proposed method.
Highlights
Biological evidence has shown that microRNAs(miRNAs) are greatly implicated in various biological progresses involved in human diseases
The study in [8] showed that a chromosomal translocation at 12q5 could influence the expression of let-7 and could cause the repress of the oncogene High Mobility Group A2(Hmga2). Another example is that mir-7 could influence epidermal growth factor receptor (EGFR) expression and protein kinase B activity in head and neck cancer(HNC) [9]
The contributions in this work are listed as follows: 1. We propose a new MDA prediction model by matrix tri-factorization model, which combines the two types of miRNA similarities, two types of disease similarities, and the known miRNA-disease associations, and predict new MDAs by completing the MDA matrix
Summary
Biological evidence has shown that microRNAs(miRNAs) are greatly implicated in various biological progresses involved in human diseases. Biological experiments indicate that miRNAs are involved in close relationships with the emergence and development processes of various human diseases [7]. The study in [8] showed that a chromosomal translocation at 12q5 could influence the expression of let-7 and could cause the repress of the oncogene High Mobility Group A2(Hmga). The study in [8] showed that a chromosomal translocation at 12q5 could influence the expression of let-7 and could cause the repress of the oncogene High Mobility Group A2(Hmga2) Another example is that mir-7 could influence epidermal growth factor receptor (EGFR) expression and protein kinase B activity in head and neck cancer(HNC) [9]. The work in [10] showed that mir-15a is a potential marker to differentiate between benign and malignant
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