Long-short term memory network for RNA structure profiling super-resolution

Pak Kan WONG; Man Leung WONG; Kwong Sak LEUNG

doi:10.1007/978-3-319-71069-3_20

Long-short term memory network for RNA structure profiling super-resolution

Pak Kan WONG, Man Leung WONG, Kwong Sak LEUNG

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings
Publisher	Springer-Verlag GmbH and Co. KG
Pages	255-266
Number of pages	12
ISBN (Electronic)	9783319710693
ISBN (Print)	9783319710686
DOIs	https://doi.org/10.1007/978-3-319-71069-3_20
Publication status	Published - Nov 2017
Event	6th International Conference on Theory and Practice of Natural Computing - Czech Republic, Prague, Czech Republic Duration: 18 Dec 2017 → 20 Dec 2017

Publication series

Name	Lecture Notes in Computer Science (LNCS) book series
Volume	10687

Conference

Conference	6th International Conference on Theory and Practice of Natural Computing
Abbreviated title	TNPC 2017
Country/Territory	Czech Republic
City	Prague
Period	18/12/17 → 20/12/17
Other	Institute of Computer Science. Czech Academy of Sciences

Funding

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).

Keywords

Long-short term memory
Machine learning regression methods
RNA structure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-71069-3_20

Cite this

WONG, P. K., WONG, M. L., & LEUNG, K. S. (2017). Long-short term memory network for RNA structure profiling super-resolution. In Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings (pp. 255-266). (Lecture Notes in Computer Science (LNCS) book series; Vol. 10687). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-71069-3_20

WONG, Pak Kan ; WONG, Man Leung ; LEUNG, Kwong Sak. / Long-short term memory network for RNA structure profiling super-resolution. Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings. Springer-Verlag GmbH and Co. KG, 2017. pp. 255-266 (Lecture Notes in Computer Science (LNCS) book series).

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title = "Long-short term memory network for RNA structure profiling super-resolution",

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.",

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WONG, PK, WONG, ML & LEUNG, KS 2017, Long-short term memory network for RNA structure profiling super-resolution. in Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings. Lecture Notes in Computer Science (LNCS) book series, vol. 10687, Springer-Verlag GmbH and Co. KG, pp. 255-266, 6th International Conference on Theory and Practice of Natural Computing, Prague, Czech Republic, 18/12/17. https://doi.org/10.1007/978-3-319-71069-3_20

Long-short term memory network for RNA structure profiling super-resolution. / WONG, Pak Kan; WONG, Man Leung; LEUNG, Kwong Sak.
Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings. Springer-Verlag GmbH and Co. KG, 2017. p. 255-266 (Lecture Notes in Computer Science (LNCS) book series; Vol. 10687).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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T1 - Long-short term memory network for RNA structure profiling super-resolution

AU - WONG, Pak Kan

AU - WONG, Man Leung

AU - LEUNG, Kwong Sak

PY - 2017/11

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N2 - 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.

AB - 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.

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BT - Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings

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ER -

WONG PK, WONG ML, LEUNG KS. Long-short term memory network for RNA structure profiling super-resolution. In Theory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings. Springer-Verlag GmbH and Co. KG. 2017. p. 255-266. (Lecture Notes in Computer Science (LNCS) book series). doi: 10.1007/978-3-319-71069-3_20

Long-short term memory network for RNA structure profiling super-resolution

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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