Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

Evolutionary Bioinformatics Online
Yulian DingFang-Xiang Wu

Abstract

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the rep...Continue Reading

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Citations

Feb 9, 2021·Briefings in Functional Genomics·Yulian DingFang-Xiang Wu
Aug 18, 2020·Methods : a Companion to Methods in Enzymology·Yulian DingFang-Xiang Wu

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Methods Mentioned

BETA
in silico methods

Software Mentioned

BioSeq
iFeature
SVM
MDA
iLearn
RWBRMDA
Analysis
DBN
PBMDA
CIPHER

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