Long-short term memory network for RNA structure profiling super-resolution
Document Type
Conference paper
Source Publication
Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings
Publication Date
1-1-2017
First Page
255
Last Page
266
Publisher
Springer Verlag
Keywords
Long-short term memory, Machine learning regression methods, RNA structure
Abstract
Profiling of RNAs improves understanding of cellular mechanisms, which can be essential to cure various diseases. It is estimated to take years to fully characterize the three-dimensional structure of around 200,000 RNAs in human using the mutate-and-map strategy. In order to speed up the profiling process, we propose a solution based on super-resolution. We applied five machine learning regression methods to perform RNA structure profiling super-resolution, i.e. to recover the whole data sets using self-similarity in low-resolution (undersampled) data sets. In particular, our novel Interaction Encoded Long-Short Term Memory (IELSTM) network can handle multiple distant interactions in the RNA sequences. When compared with ridge regression, LASSO regression, multilayer perceptron regression, and random forest regression, IELSTM network can reduce the mean squared error and the median absolute error by at least 33% and 31% respectively in three RNA structure profiling data sets.
DOI
10.1007/978-3-319-71069-3_20
Print ISSN
03029743
E-ISSN
16113349
Funding Information
This research is supported by General Research Fund (LU310111 and 414413) from the Research Grant Council of the Hong Kong Special Administrative Region and the Lingnan University Direct Grant (DR16A7). {LU310111, 414413, DR16A7}
Publisher Statement
Copyright © Springer International Publishing AG 2017. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 6th International Conference on Theory and Practice of Natural Computing (TPNC 2017), 18-20 December 2017, Prague, Czech Republic.
ISBN of the source publication: 9783319710686
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Wong, P.-K., Wong, M.-L., & Leung, K.-S. (2017). Long-short term memory network for RNA structure profiling super-resolution. In C. Martín-Vide, R. Neruda, & M. A. Vega-Rodríguez (Eds.), Theory and Practice of Natural Computing: 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings (pp. 255-266). Switzerland: Springer Verlag. doi: 10.1007/978-3-319-71069-3_20