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

Pak Kan WONG, Man Leung WONG, Kwong Sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)


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 languageEnglish
Title of host publicationTheory and Practice of Natural Computing : 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, proceedings
PublisherSpringer-Verlag GmbH and Co. KG
Number of pages12
ISBN (Electronic)9783319710693
ISBN (Print)9783319710686
Publication statusPublished - Nov 2017
Event6th International Conference on Theory and Practice of Natural Computing - Czech Republic, Prague, Czech Republic
Duration: 18 Dec 201720 Dec 2017

Publication series

NameLecture Notes in Computer Science (LNCS) book series


Conference6th International Conference on Theory and Practice of Natural Computing
Abbreviated titleTNPC 2017
Country/TerritoryCzech Republic
OtherInstitute of Computer Science. Czech Academy of Sciences


  • Long-short term memory
  • Machine learning regression methods
  • RNA structure


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