Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning

Determining microRNA (miRNA)-disease associations (MDAs) is an integral part in the prevention, diagnosis, and treatment of complex diseases. However, wet experiments to discern MDAs are inefficient and expensive. Hence, the development of reliable and efficient data integrative models for predictin...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 34; číslo 9; s. 5570 - 5579
Hlavní autoři: Zhou, Feng, Yin, Meng-Meng, Jiao, Cui-Na, Zhao, Jing-Xiu, Zheng, Chun-Hou, Liu, Jin-Xing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Deep autoencoder (DAE)
Deep learning
Disease
Diseases
Feature extraction
feature representation
Identification methods
Kernel
Kernels
microRNA (miRNA)–disease associations (MDAs)
MicroRNAs
miRNA
multiple kernel learning (MKL)
Performance prediction
Predictive models
Regression models
Representations
Ribonucleic acid
RNA
Semantics
Similarity
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Determining microRNA (miRNA)-disease associations (MDAs) is an integral part in the prevention, diagnosis, and treatment of complex diseases. However, wet experiments to discern MDAs are inefficient and expensive. Hence, the development of reliable and efficient data integrative models for predicting MDAs is of significant meaning. In the present work, a novel deep learning method for predicting MDAs through deep autoencoder with multiple kernel learning (DAEMKL) is presented. Above all, DAEMKL applies multiple kernel learning (MKL) in miRNA space and disease space to construct miRNA similarity network and disease similarity network, respectively. Then, for each disease or miRNA, its feature representation is learned from the miRNA similarity network and disease similarity network via the regression model. After that, the integrated miRNA feature representation and disease feature representation are input into deep autoencoder (DAE). Furthermore, the novel MDAs are predicted through reconstruction error. Ultimately, the AUC results show that DAEMKL achieves outstanding performance. In addition, case studies of three complex diseases further prove that DAEMKL has excellent predictive performance and can discover a large number of underlying MDAs. On the whole, our method DAEMKL is an effective method to identify MDAs.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3129772