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Neural Computing and Applications
Published in: Neural Computing and Applications 3/2018

19-07-2016 | Original Article

A computational approach for nuclear export signals identification using spiking neural P systems

Authors: Zhihua Chen, Pan Zhang, Xun Wang, Xiaolong Shi, Tingfang Wu, Pan Zheng

Published in: Neural Computing and Applications | Issue 3/2018

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Abstract

Nuclear export signal (NES) is a nuclear targeting signal within cargo proteins, which is involved in signal transduction and cell cycle regulation. NES is believed to be “born to be weak”; hence, it is a challenge in computational biology to identify it from high-throughput data of amino acid sequences. This work endeavors to tackle the challenge by proposing a computational approach to identifying NES using spiking neural P (SN P) systems. Specifically, secondary structure elements of 30 experimentally verified NES are randomly selected for training an SN P system, and then 1224 amino acid sequences (containing 1015 regular amino acid sequences and 209 experimentally verified NES) abstracted from 221 NES-containing protein sequences randomly in NESdb are selected to test our method. Experimental results show that our method achieves a precision rate 75.41 %, better than NES-REBS 47.2 %, Wregex 25.4 %, ELM, and NetNES 37.4 %. The results of this study are promising in terms of the fact that it is the first feasible attempt to use SN P systems in computational biology after many theoretical advancements.
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Appendix
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Metadata
Title
A computational approach for nuclear export signals identification using spiking neural P systems
Authors
Zhihua Chen
Pan Zhang
Xun Wang
Xiaolong Shi
Tingfang Wu
Pan Zheng
Publication date
19-07-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 3/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2489-z

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