Journal
NEURAL COMPUTING & APPLICATIONS
Volume 29, Issue 3, Pages 695-705Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-016-2489-z
Keywords
Nuclear export signal; Bio-inspired computing model; Spiking neural P system
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Funding
- National Natural Science Foundation of China [61272152, 61370105, 61402187, 61502535, 61572522, 61572523]
- China Postdoctoral Science Foundation [2016M592267]
- Program for New Century Excellent Talents in University [NCET-13-1031]
- 863 Program [2015AA020925]
- Fundamental Research Funds for the Central Universities [R1607005A]
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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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