A Stacked Autoencoder-Based miRNA Regulatory Module Detection Framework

DOI

10.2991/ijcis.d.190718.002How to use a DOI?

Keywords

Module detection; Intimacy; K-means; Stacked autoencoder

Abstract

MicroRNA regulatory module (MRM) plays an important role in the study of microRNA synergism. To detect MRMs, researchers have developed a number of related methods in the preceding decades. However, some existing methods are stochastic or specific to a certain situation. In this paper, we presented a novel deep ensemble framework called DeMosa to identify MRM for different cancers. In the proposed framework, we integrated stacked autoencoders and K-means method to detect MRMs in high-dimensional complex biological networks. We tested our method on synthetic data and three types of cancer data sets. In the synthetic data, we found DeMosa is superior to existing three methods SNMNMF, Mirsynergy, and bi-cliques merging (BCM) on clustering accuracy, stability, and module quality, while in the cancer datasets, DeMosa is more adaptable in different situations than the counterparts. In addition, we applied Kaplan–Meier survival analysis to predict several MRMs as potential prognostic biomarkers in cancers.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yi Yang
AU  - Yan Song
PY  - 2019
DA  - 2019/07/18
TI  - A Stacked Autoencoder-Based miRNA Regulatory Module Detection Framework
JO  - International Journal of Computational Intelligence Systems
SP  - 822
EP  - 832
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190718.002
DO  - 10.2991/ijcis.d.190718.002
ID  - Yang2019
ER  -