A Learning-Based Framework for Identifying MicroRNA Regulatory Module

Authors

Yi Yang^*,

College of Information Science and Engineering, Hunan Women’s University, No. 160, Zhongyi First Road, Changsha, 410004, China

^*Email: snryou@126.com

DOI

10.2991/ijcis.d.201009.001How to use a DOI?

Keywords

MicroRNA regulatory module; Convolutional autoencoder; K-means; MicroRNA-target interaction

Abstract

Accurate identification of microRNA regulatory modules can give insights to understand microRNA synergistical regulatory mechanism. However, the identification accuracy suffers from incomplete biological data. In this paper, we proposed a learning-based framework called MicroRNA regulatory module dentification with Convolutional Autoencoders (MICA). Firstly, the framework applied convolutional autoencoders to extract significant features of microRNA and their target-genes. Then they were clustered into microRNA clusters and target-gene clusters. Finally, the two types of clusters were combined into modules by known microRNA–target interactions. Compared with three existing methods on three cancer data sets, the modules detected by the proposed method exhibited better overall performance.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

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
PY  - 2020
DA  - 2020/10/16
TI  - A Learning-Based Framework for Identifying MicroRNA Regulatory Module
JO  - International Journal of Computational Intelligence Systems
SP  - 1598
EP  - 1607
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201009.001
DO  - 10.2991/ijcis.d.201009.001
ID  - Yang2020
ER  -